EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Maier, Jennifer; Bergler, Christian; Kröger, Christine; Voigt, Jörn; Breininger, Katharina; Klopfleisch, Robert; Maier, Andreas (Scientific Reports, 2021)
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
CRIS-Team Scopus Importer:2021-04-09

Exhaustive Graph Cut-based Vasculature Reconstruction - Unberath, Mathias; Achenbach, Stephan; Fahrig, Rebecca; Maier, Andreas (Proceedings of the ISBI, 2016)
We propose an exhaustive extension to graph cut-based coronary artery reconstruction from multiple views of a rotational angiography sequence. The reconstruction is formulated as an energy minimization problem that is solved using the a-expansion algorithm. We enforce reprojection-based data consistency and completeness conditions on the reconstructed centerline. The proposed strategy omits the need for selection and manual refinement of a reference view. A phantom study is used to assess the performance in 2D and 3D. The average reprojection error decreased from 1.32 ± 0.99mm to 0.54 ± 0.02 mm. Moreover, an increase in Dice score from 0.50 ± 0.04 mm to 0.59 ± 0.00 mm was observed, indicating superior volumetric reconstruction quality. The results suggest that the proposed extension removes the susceptibility to reference frame selection and manual interaction, while increasing reconstruction quality.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.exhaus_2
Keywords: C-arm CT; Cardiac Dynamics; Cardiac Imaging; Epipolar Geometry; Graph Cut; Symmetrization

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy - Thies, Mareike; Wagner, Fabian; Gu, Mingxuan; Mei, Siyuan; Huang, Yixing; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Uderhardt, Stefan; Schett, Georg; Christiansen, Silke; et al. (Informatik aktuell, 2024)
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bones. However, respiratory motion and muscle relaxation lead to inconsistencies in the projection data which result in artifacts in uncompensated reconstructions. Motion compensation using epipolar consistency conditions (ECC) has previously shown good performance in clinical CT settings. Here, we explore whether such algorithms are suitable for correcting motion-corrupted XRM data. Different rigid motion patterns are simulated and the quality of the motion-compensated reconstructions is assessed. The method is able to restore microscopic features for out-of-plane motion, but artifacts remain for more realistic motion patterns including all six degrees of freedom of rigid motion. Therefore, ECC is valuable for the initial alignment of the projection data followed by further fine-tuning of motion parameters using a reconstruction-based method.
CRIS-Team Scopus Importer:2024-03-29

Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - Zaiss, Moritz; Rajput, Junaid R.; Dang, Hoai N.; Golkov, Vladimir; Cremers, Daniel; Knoll, Florian; Maier, Andreas (Informatik aktuell, 2024)
In this study, we explore the potential of generative pre-trained transformer (GPT), as a coding assistant for MRI sequence programming using the Pulseq framework. The programming of MRI sequences is traditionally a complex and time-consuming task, and the Pulseq standard has recently simplified this process. It allows researchers to define and generate complex pulse sequences used in MRI experiments. Leveraging GPT-4’s capabilities in natural language generation, we adapted it for MRI sequence programming, creating a specialized assistant named GPT4MR. Our tests involved generating various MRI sequences, revealing that GPT-4, guided by a tailored prompt, outperformed GPT-3.5, producing fewer errors and demonstrating improved reasoning. Despite limitations in handling complex sequences, GPT4MR corrected its own errors and successfully generated code with step-by-step instructions. The study showcases GPT4MR’s ability to accelerate MRI sequence development, even for novel ideas absent in its training set. While further research and improvement are needed to address complexity limitations, a well-designed prompt enhances performance. The findings propose GPT4MR as a valuable MRI sequence programming assistant, streamlining prototyping and development. The future prospect involves integrating a PyPulseq plugin into lightweight, open-source LLMs, potentially revolutionizing MRI sequence development and prototyping.
CRIS-Team Scopus Importer:2024-03-29

Exploring the Limitations of Hybrid Adiabatic Quantum Computing for Emission Tomography Reconstruction - Nau, Merlin; Vija, Alexander Hans; Gohn, Wesley; Reymann, Maximilian; Maier, Andreas (Journal of Imaging, 2023)
Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a L2 norm. After reviewing quantum computing principles, we propose the use of a commercially available quantum annealer and employ corresponding hybrid solvers, which combine quantum and classical computing to handle more significant problems. We demonstrate how to frame image reconstruction as a combinatorial optimization problem suited for these quantum annealers and hybrid systems. Using a toy problem, we analyze reconstructions of binary and integer-valued images with respect to their image size and compare them to conventional methods. Additionally, we test our method’s performance under noise and data underdetermination. In summary, our method demonstrates competitive performance with traditional algorithms for binary images up to an image size of 32 by 32 on the toy problem, even under noisy and underdetermined conditions. However, scalability challenges emerge as image size and pixel bit range increase, restricting hybrid quantum computing as a practical tool for emission tomography reconstruction until significant advancements are made to address this issu
Keywords: image reconstruction; quantum computing; quantum annealing; tomographic imaging; emission tomography

Exploring the Open World Using Incremental Extreme Value Machines - Koch, Tobias; Liebezeit, Felix; Rieß, Christian; Christlein, Vincent; Köhler, Thomas (2022 26th International Conference on Pattern Recognition (ICPR), 2022)
Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces a modification of the widely known Extreme Value Machine (EVM) to enable open world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. This reduces the training time by a factor of 28. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognitio
Keywords: classification and clustering; online learning; continual learning

Exploring the space between smoothed and non-smooth total variation for 3D iterative CT reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Because total variation (TV) is non-differentiable, iterative reconstruction using a TV penalty comes with technical difficulties. To avoid these, it is popular to use a smooth approximation of TV instead, which is defined using a single parameter, herein called δ, with the convention that the approximation is better when δ is smaller. To our knowledge, it is not known how well image reconstruction with this approximation can approach a converged non-smooth TV-regularized result. In this work, we study this particular question in the context of X-ray computed tomography (CT). Experimental results are reported with real CT data of a head phantom and supplemented with a theoretical analysis. To address our question, we make proficient use of a generalized iterative soft-thresholding algorithm that allows us to handle TV and its smooth approximation in the same framework. Our results support the following conclusions. First, images reconstructed using the smooth approximation of TV appears to smoothly converge towards the TV result as δ tends to zero. Second, the value of δdoes not need to be overly small to obtain a result that is essentially equivalent to TV, implying that numerical instabilities can be avoided. Last, though it is smooth, the convergence with δ is not particularly fast, as the mean absolute pixel difference decreases only as √δ in our experiments. Altogether, we conclude that the approximation is a theoretically valid way to approximate the non-smooth TV penalty for CT, opening the door to safe utilization of a wide variety of optimization algorithms.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Computed tomography; GISTA; Huber function; Iterative reconstruction; Total variation

Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation - Gündel, Sebastian; Setio, Arnaud; Grbic, Sasa; Maier, Andreas; Comaniciu, Dorin (Machine Learning in Medical Imaging, 2020)
Chest X-ray (CXR) is the most common examination for fast detection of pulmonary abnormalities. Recently, automated algorithms have been developed to classify multiple diseases and abnormalities in CXR scans. However, because of the limited availability of scans containing nodules and the subtle properties of nodules in CXRs, state-of-the-art methods do not perform well on nodule classification. To create additional data for the training process, standard augmentation techniques are applied. However, the variance introduced by these methods are limited as the images are typically modified globally. In this paper, we propose a method for local feature augmentation by extracting local nodule features using a generative inpainting network. The network is applied to generate realistic, healthy tissue and structures in patches containing nodules. The nodules are entirely removed in the inpainted representation. The extraction of the nodule features is processed by subtraction of the inpainted patch from the nodule patch. With arbitrary displacement of the extracted nodules in the lung area across different CXR scans and further local modifications during training, we significantly increase the nodule classification performance and outperform state-of-the-art augmentation methods.<br
Keywords: Nodule classification; Local feature augmentation; Context encoder; Chest X-ray

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks - Meister, Felix; Passerini, Tiziano; Audigier, Chloé; Lluch, Èric; Mihalef, Viorel; Ashikaga, Hiroshi; Maier, Andreas; Halperin, Henry; Mansi, Tommaso (Frontiers in Physiology, 2021)
Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.
Keywords: deep learning; graph convolutional networks; cardiac computational modeling; electroanatomic mapping; sparse measurements

Face mask recognition from audio: The MASC database and an overview on the mask challenge - Mohamed, Mostafa M.; Nessiem, Mina A.; Batliner, Anton; Bergler, Christian; Hantke, Simone; Schmitt, Maximilian; Baird, Alice; Mallol-Ragolta, Adria; Karas, Vincent; Amiriparian, Shahin; Schuller, Bjorn W. (Pattern Recognition, 2021)
The sudden outbreak of COVID-19 has resulted in tough challenges for the field of biometrics due to its spread via physical contact, and the regulations of wearing face masks. Given these constraints, voice biometrics can offer a suitable contact-less biometric solution; they can benefit from models that classify whether a speaker is wearing a mask or not. This article reviews the Mask Sub-Challenge (MSC) of the INTERSPEECH 2020 COMputational PARalinguistics challengE (ComParE), which focused on the following classification task: Given an audio chunk of a speaker, classify whether the speaker is wearing a mask or not. First, we report the collection of the Mask Augsburg Speech Corpus (MASC) and the baseline approaches used to solve the problem, achieving a performance of 71.8% Unweighted Average Recall (UAR). We then summarise the methodologies explored in the submitted and accepted papers that mainly used two common patterns: (i) phonetic-based audio features, or (ii) spectrogram representations of audio combined with Convolutional Neural Networks (CNNs) typically used in image processing. Most approaches enhance their models by adapting ensembles of different models and attempting to increase the size of the training data using various techniques. We review and discuss the results of the participants of this sub-challenge, where the winner scored a UAR of 80.1%. Moreover, we present the results of fusing the approaches, leading to a UAR of 82.6%. Finally, we present a smartphone app that can be used as a proof of concept demonstration to detect in real-time whether users are wearing a face mask; we also benchmark the run-time of the best models.<br
Keywords: COVID-19; Deep learning; Masks; Voice biometrics; Acoustic modelling

Factors affecting accuracy of S values and determination of time-integrated activity in clinical Lu-177 dosimetry - Götz, Th I.; Schmidkonz, Christian; Lang, E. W.; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Annals of Nuclear Medicine, 2019)
Introduction: In any radiotherapy, the absorbed dose needs to be estimated based on two factors, the time-integrated activity of the administered radiopharmaceutical and the patient-specific dose kernel. In this study, we consider the uncertainty with which such absorbed dose estimation can be achieved in a clinical environment. Methods: To calculate the total error of dose estimation we considered the following aspects: The error resulting from computing the time-integrated activity, the difference between the S-value and the patient specific full Monte Carlo simulation, the error from segmenting the volume-of-interest (kidney) and the intrinsic error of the activimeter. Results: The total relative error in dose estimation can amount to 25.0% and is composed of the error of the time-integrated activity 17.1%, the error of the S-value 16.7%, the segmentation error 5.4% and the activimeter accuracy 5.0%. Conclusion: Errors from estimating the time-integrated activity and approximations applied to dose kernel computations contribute about equally and represent the dominant contributions far exceeding the contributions from VOI segmentation and activimeter accuracy.
CRIS-Team Scopus Importer:2019-06-18
Keywords: Lu-177 dosimetry; Monte Carlo simulations; S-values; SPECT/CT; Time integrated activity

Failure and Risk Analysis based on Maintenance Reports of Machines Components in Manufacturing Industry - Thamm, Aleksandra; Herz, Matthias; Wiedemann, Markus; Thamm, Florian; Maier, Andreas (XIII. International Conference on the Theory of Machines and Mechanisms - TMM2020, 2021)
Manufacturing engineering is concerned with the application of engineering practices in manufacturing processes and production methods. Continuous optimization of processes, tools as well as machinery and equipment is the key to producing quality products with optimal capital investment. Hereby, maintenance plays a crucial role and allows manufacturers to optimize their processes. To enable improvements in this field it is required to analyze and evaluate the necessities for actions on component level. In this work we want to present a detailed retrospective analysis of more than 4.000 maintenance reports and analyze causes for assembly failures depending on the associated components. Furthermore, we extend the analysis and investigate the failure dependency with respect to the days elapsed from the date of maintenance request to resolving the issue. It turned out, not all cases with high failure rates are associated with subsequent higher risks considering the respective urgency related to each failure case. Furthermore, we discovered especially hydraulic and pneumatic components have a significant high to critical risk, as the labour safety and other components are threatened by their failure. Moreover we determined, that only 10% of all actions were planned maintenance and thus we propose based on the results of the risks analysis, how planned and preventive maintenance should be extended to reduce costs in this regard.<br
Keywords: Manufacturing; Failure Analysis; Risk Analysis; Spare Parts; Maintenance; Preventive Maintenance

Fan-beam Projection Image Acquisition using MRI - Syben-Leisner, Christopher; Stimpel, Bernhard; Leghissa, Martino; Dörfler, Arnd; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fanbea_5

Fan-to-Parallel Beam Conversion: Deriving Neural Network Architectures Using Precision Learning - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (Informatik aktuell, 2019)
CRIS-Team Scopus Importer:2019-05-14

Fast 3D YOLOv3 based standard plane regression of vertebral bodies in intra-operative CBCT volumes - Doerrich, Sebastian; Kordon, Florian Johannes; Denzinger, Felix; El Barbari, Jan S.; Privalov, Maxim; Vetter, Sven Y.; Maier, Andreas; Kunze, Holger (Journal of Medical Imaging, 2023)
Purpose Mobile C-arm systems represent the standard imaging devices within the field of spine surgery. In addition to 2D imaging, they allow for 3D scans while preserving unrestricted patient access. For viewing, the acquired volumes are adjusted such that their anatomical standard planes align with the axes of the viewing modality. This difficult and time-consuming step is currently performed manually by the leading surgeon. This process is automatized within this work to improve the usability of C-arm systems. Thereby, the spinal region consisting of multiple vertebrae and the standard planes of all vertebrae being of interest to the surgeon need to be taken into account. Approach An object detection algorithm based on the you only look once version 3 architecture, adapted to 3D inputs, is compared with a segmentation-based approach employing a 3D U-Net. Both algorithms are trained on a dataset of 440 and tested on 218 spinal volumes. Results Although the detection-based algorithm is slightly inferior concerning the detection (91% versus 97% accuracy), localization (1.26 mm versus 0.74 mm error) and alignment accuracy (5.00 deg versus 4.73 deg error), it outperforms the segmentation-based one in terms of speed (5 s versus 38 s). Conclusions Both algorithms show similar good results. However, the speed gain of the detection-based algorithm, resulting in a run time of 5 s, makes it more suitable for usage in an intra-operative scenari

Fast Adaptive Regularization for Perfusion Parameter Computation - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.fastad_9

Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Informatik aktuell, 2015)
Computation of perfusion parameters by deconvolution from contrast-enhanced time-resolved CT or MR perfusion data sets is an illconditioned problem. Thus, adequate regularization and determination of corresponding regularization parameters is required. We present a novel method for Tikhonov regularization for perfusion imaging to locally adapt parameters to the SNR level by using a regression function. In an numerical evaluation our simple approach provided similar or even superior results compared to methods applying computationally more demanding L-curve analysis.
CRIS-Team Scopus Importer:2022-08-05

Fast and Efficient Limited Data Hyperspectral Remote Sensing Image Classification via GMM-Based Synthetic Samples - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Rieß, Christian (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019)

Fast and Robust Detection of Solar Modules in Electroluminescence Images - Hoffmann, Mathis; Doll, Bernd; Talkenberg, Florian; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (Computer Analysis of Images and Patterns, 2019)

Fast and robust selection of highly-correlated features in regression problems - Maier, Andreas; Rodriguez Salas, Dalia (2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 2017)
Feature selection for regression problems can be highly beneficial in terms of robustness and execution speed. The Correlation-based Feature Selection (CFS) algorithm, in the attempt to find the best feature subset, evaluates different subsets and selects the one with the highest “goodness”. Such goodness is based on the co-relation between the addition of all features in the subset with the output variable. However, such a simple addition assumes that all features have the same weight in the output variable. This, in turn, assumes that all features are uncorrelated with each other. By considering an optimal weighting instead, a more robust measurement of the goodness can be obtained; however, such weighting is computationally expensive. In this paper, a feature selection algorithm named “R-fast” which considers the β coefficients in the standardized linear regression model as optimal weights is proposed. We also present a technique to quickly estimate approximations to the β coefficients. Our algorithm was evaluated using Multiple Regression Analysis (MRA) over 8 synthetic and 10 real-world datasets. Results show that, when selecting features with our proposed algorithm, MRA's performance is better than or equal to those obtained when selecting features with others well-known filter algorithms and when no selection is performed.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fastan
Keywords: Feature Selection; Regression Analysis

Fast Automatic Liver Tumor Radiofrequency Ablation Planning via Learned Physics Model - Meister, Felix; Audigier, Chloé; Passerini, Tiziano; Lluch, Èric; Mihalef, Viorel; Maier, Andreas; Tommaso, Mansi (25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VII, 2022)
Radiofrequency ablation is a minimally-invasive therapy recommended for the treatment of primary and secondary liver cancer in early stages and when resection or transplantation is not feasible. To significantly reduce chances of local recurrences, accurate planning is required, which aims at finding a safe and feasible needle trajectory to an optimal electrode position achieving full coverage of the tumor as well as a safety margin. Computer-assisted algorithms, as an alternative to the time-consuming manual planning performed by the clinicians, commonly neglect the underlying physiology and rely on simplified, spherical or ellipsoidal ablation estimates. To drastically speed up biophysical simulations and enable patient-specific ablation planning, this work investigates the use of non-autoregressive operator learning. The proposed architecture, trained on 1,800 biophysics-based simulations, is able to match the heat distribution computed by a finite-difference solver with a root mean squared error of 0.51 ± .50 ∘C and the estimated ablation zone with a mean dice score of 0.93 ± 0.05, while being over 100 times faster. When applied to single electrode automatic ablation planning on retrospective clinical data, our method achieves patient-specific results in less than 4 mins and closely matches the finite-difference-based planning, while being at least one order of magnitude faster. Run times are comparable to those of sphere-based planning while accounting for the perfusion of liver tissue and the heat sink effect of large vessels.<br

Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses - Preuhs, Alexander; Manhart, Michael; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.fastep

Fast Interpolation of Dense Motion Fields from Synthetic Phantoms - Maier, Andreas; Taubmann, Oliver; Wetzl, Jens; Wasza, Jakob; Forman, Christoph; Fischer, Peter; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2014, 2014)
Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 643 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 2563 dense deformation field to only 6.5 minutes using the proposed methods from days with previous methods.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.fastin_7
Keywords: interpolation; motion field; phantom

Fast online-customized (FOCUS) parallel transmission pulses: A combination of universal pulses and individual optimization - Herrler, Jürgen; Liebig, Patrick; Gumbrecht, Rene; Ritter, Dieter; Schmitter, Sebastian; Maier, Andreas; Schmidt, Manuel; Uder, Michael; Dörfler, Arnd; Nagel, Armin Michael (Magnetic Resonance in Medicine, 2021)
Purpose: To mitigate spatial flip angle (FA) variations under strict specific absorption rate (SAR) constraints for ultra-high field MRI using a combination of universal parallel transmit (pTx) pulses and fast subject-specific optimization. Methods: Data sets consisting of B0, (Formula presented.) maps, and virtual observation point (VOP) data were acquired from 72 subjects (study groups of 48/12 healthy Europeans/Asians and 12 Europeans with pathological or incidental findings) using an 8Tx/32Rx head coil on a 7T whole-body MR system. Combined optimization values (COV) were defined as combination of spiral-nonselective (SPINS) trajectory parameters and an energy regularization weight. A set of COV was optimized universally by simulating the individual RF pulse optimizations of 12 training data sets (healthy Europeans). Subsequently, corresponding universal pulses (UPs) were calculated. Using COV and UPs, individually optimized pulses (IOPs) were calculated during the sequence preparation phase (maximum 15 s). Two different UPs and IOPs were evaluated by calculating their normalized root-mean-square error (NRMSE) of the FA and SAR in simulations of all data sets. Seven additional subjects were examined using an MPRAGE sequence that uses the designed pTx excitation pulses and a conventional adiabatic inversion. Results: All pTx pulses resulted in decreased mean NRMSE compared to a circularly polarized (CP) pulse (CP = 28%, UPs = 17%, and IOPs = 12%). UPs and IOPs improved homogeneity for all subjects. Differences in NRMSE between study groups were much lower than differences between different pulse types. Conclusion: UPs can be used to generate fast online-customized (FOCUS) pulses gaining lower NRMSE and/or lower SAR values.
CRIS-Team Scopus Importer:2021-01-15
Keywords: 7 T; k; parallel transmission (pTx); SPINS; ultra-high-field MRI; universal pulses

Fast Pose Verification for High-Speed Radiation Therapy - Maier, Andreas; Westphal, Susanne; Geimer, Tobias; Maxim, Peter G.; King, Gregory; Schueler, Emil; Fahrig, Rebecca; Loo, Billy (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses fast pose verification for radiation therapy on a new high-speed radiation therapy device. The PHASER system follows the idea of 4th generation CT imaging and allows fast 360° treatment using a steerable electron beam. Doing so, dose delivery is possible in few seconds. A major problem, however, is fast verification of the patient pose during treatment. In this paper, we suggest to use a projection-based approach that can be evaluated quickly and allows an accuracy below 1 mm as shown by our simulation study based on planning data from six 4D CT data sets.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fastpo

Fast, Robust and Efficient Extraction of Book Pages from a 3-D X-ray CT Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fastro

Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Riess, Christian (IEEE International Geoscience and Remote Sensing Symposium, 2018)

Fast simulation of x-ray projections of spline-based surfaces using an append buffer - Maier, Andreas; Hofmann, Hannes; Schwemmer, Chris; Hornegger, Joachim; Keil, Andreas; Fahrig, Rebecca (Physics in Medicine and Biology, 2012)
Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, the simulation speed can be increased dramatically using state-of-the-art graphics hardware. A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel. Projection of a moving spline structure is fast and accurate. Projections of size 640×480 can be generated within 254ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9HU on average, although projection images appear to be correct under visual examination. Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically. © 2012 Institute of Physics and Engineering in Medicine.

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification - Wilm, Frauke; Benz, Michaela; Bruns, Volker; Baghdadlian, Serop; Dexl, Jakob; Hartmann, David; Kuritcyn, Petr; Weidenfeller, Martin; Wittenberg, Thomas; Merkel, Susanne; Hartmann, Arndt; Eckstein, Markus; et al. (Journal of Medical Imaging, 2022)
Purpose: Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSIs), however, poses a challenge in terms of computation time. In this regard, the analysis of nonoverlapping patches outperforms pixelwise segmentation approaches but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies.
CRIS-Team WoS Importer:2022-05-13

Fat Content and Fatty Acid Composition Quantification Using a 3D Stack-of-Radial Trajectory With Adaptive Gradient Calibration - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fatcon

Fat-Saturated MR Image Synthesis with Acquisition Parameter-Conditioned Image-to-Image Generative Adversarial Network - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (International Society for Magnetic Resonance in Medicine (ISMRM) 29th Annual Meeting & Exhibition, 2021)

Fault and anomaly detection in district heating substations: A survey on methodology and data sets - Neumayer, Martin; Stecher, Dominik; Grimm, Sebastian; Maier, Andreas; Bücker, Dominikus; Schmidt, Jochen (Energy, 2023)
District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks.
CRIS-Team Scopus Importer:2023-05-05
Keywords: District heating systems; Fault/anomaly detection; Machine learning

Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study - Sievert, Matti; Oetter, Nicolai; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Eckstein, Markus; Mantsopoulos, Konstantinos; Gostian, Antoniu Oreste; Mueller, Sarina K.; Koch, Michael; Agaimy, Abbas; Iro, Heinrich; et al. (Auris Nasus Larynx, 2021)
Objective: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with Confocal Laser Endomicroscopy (CLE) during planned partial or total laryngectomy. Methods: Eight patients with confirmed larynx squamous cell carcinoma (SCC) and planned partial or total laryngectomy were included in this study in March 2020. Two head and neck surgeons and one pathologist were asked to classify carcinoma or healthy epithelium in a sample of 94 representative sequences (5.640 images), blinded to the histological results (H&E staining). Results: Healthy mucosa areas showed epithelium with cells of uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of SCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 80.1%, 72.3%, 87.9%, 85.7%, and 76.1%, respectively. A distinct transition between healthy appearing tissue and suspicious lesions could also be detected. Conclusion: CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the larynx for evaluation and demarcation of cancer. If validated in further studies, CLE could eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
CRIS-Team Scopus Importer:2021-01-29
Keywords: Confocal laser endomicroscopy; Laryngeal cancer; Safe surgical margins

Feasibility of Motion Compensation using Inertial Measurement in C-arm CT - Maier, Jennifer; Aichert, André; Mehringer, Wolfgang; Bier, Bastian; Eskofier, Björn; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Bonaretti, Serena; Maier, Andreas (Conference Record of the 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2018)

Feasibility Study: 2-D Self-Navigation using Compressed Sensing Reconstruction for Respiratory Gating in Free-breathing 3-D CINE Imaging - Prochaska, Ivo; Wetzl, Jens; Forman, Christoph; Nagel, Armin Michael; Maier, Andreas (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.feasib

Feasibility Study: Free-Breathing 3-D CINE Imaging with Respiratory Gating Based on Pilot Tone Navigation - Wetzl, Jens; Schroeder, Lea; Forman, Christoph; Lugauer, Felix; Rehner, Robert; Fenchel, Matthias; Maier, Andreas; Hornegger, Joachim; Speier, Peter (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.feasib

Feature Loss After Denoising of SPECT Projection Data using a U-Net - Reymann, Maximilian; Massanes, Francesc; Ritt, Philipp; Cachovan, Michal; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (Feature Loss After Denoising of SPECT Projection Data using a U-Net, 2020)
It has been long known that denoising of SPECT projection data from multi channel collimators harbors the danger that features are filtered away as distinction between noise and signal in the 2D projection image does not consider the tomographic information (see Vija, Yahil et al.). We investigate if an approach using Neural Networks can overcome the past issues, by training a U-Net on Monte Carlo Simulations of the XCAT phantom. Although error metrics indicate improvement of signal statistics, we observe a loss of image features after the reconstruction. We show that when using OSEM reconstruction without denoising in projection domain, these features can be preserved.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Federated learning for secure development of AI models for Parkinson’s disease detection using speech from different languages - Tayebi Arasteh, Soroosh; Rios-Urrego, Cristian David; Nöth, Elmar; Maier, Andreas; Yang, Seung Hee; Rusz, Jan; Rafael Orozco-Arroyave, Juan (Proceedings of INTERSPEECH 2023, 2023)
Parkinson's disease (PD) is a neurological disorder impacting a person's speech. Among automatic PD assessment methods, deep learning models have gained particular interest. Recently, the community has explored cross-pathology and cross-language models which can improve diagnostic accuracy even further. However, strict patient data privacy regulations largely prevent institutions from sharing patient speech data with each other. In this paper, we employ federated learning (FL) for PD detection using speech signals from 3 real-world language corpora of German, Spanish, and Czech, each from a separate institution. Our results indicate that the FL model outperforms all the local models in terms of diagnostic accuracy, while not performing very differently from the model based on centrally combined training sets, with the advantage of not requiring any data sharing among collaborators. This will simplify inter-institutional collaborations, resulting in enhancement of patient outcomes.
Keywords: federated learning; speech pathology; Parkinson’s disease; deep learning; trustworthy speech processing

Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning - Gabler, Elisabeth; Nissen, Michael; Altstidl, Thomas Robert; Titzmann, Adriana; Packhäuser, Kai; Maier, Andreas; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL-PLANES-DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and explainability.Clinical relevance - To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
Created from Fastlane, Scopus look-up
Keywords: Pregnancy; Deep learning; Visualization; Ultrasonic imaging; Fetus; Stability analysis; Biology

Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation - Gu, Mingxuan; Vesal, Sulaiman; Kosti, Ronak Vijaypal; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty - Gao, Cong; Farvardin, Amirhossein; Grupp, Robert; Bakhtiarinejad, Mahsan; Ma, Liuhong; Thies, Mareike; Unberath, Mathias; Taylor, Russell; Armand, Mehran (IEEE Transactions on Medical Robotics and Bionics, 2020)
Femoroplasty is a proposed alternative therapeutic method for preventing osteoporotic hip fractures in the elderly. Previously developed navigation system for femoroplasty required the attachment of an external X-ray fiducial to the femur. We propose a fiducial-free 2D/3D registration pipeline using fluoroscopic images for robot-assisted femoroplasty. Intraoperative fluoroscopic images are taken from multiple views to perform registration of the femur and drilling/injection device. The proposed method was tested through comprehensive simulation and cadaveric studies. Performance was evaluated on the registration error of the femur and the drilling/injection device. In simulations, the proposed approach achieved a mean accuracy of 1.26±0.74 mm for the relative planned injection entry point; 0.63 ± 0.21◦ and 0.17 ± 0.19◦ for the femur injection path direction and device guide direction, respectively. In the cadaver studies, a mean error of 2.64±1.10 mm was achieved between the planned entry point and the device guide tip. The biomechanical analysis showed that even with a 4 mm translational deviation from the optimal injection path, the yield load prior to fracture increased by 40.7%. This result suggests that the fiducial-less 2D/3D registration is sufficiently accurate to guide robot assisted femoroplasty.<br
Keywords: 2D/3D registration; femur registration; X-ray navigation; robot-assisted femoroplasty

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment - Choi, Jang-Hwan; Maier, Andreas; Keil, Andreas; Pal, Saikat; McWalter, Emily; Beaupre, Gary; Gold, Garry; Fahrig, Rebecca (Medical Physics, 2014)
Purpose: A C-arm CT system has been shown to be capable of scanning a single cadaver leg under loaded conditions by virtue of its highly flexible acquisition trajectories. In Part I of this study, using the 4D XCAT-based numerical simulation, the authors predicted that the involuntary motion in the lower body of subjects in weight-bearing positions would seriously degrade image quality and the authors suggested three motion compensation methods by which the reconstructions could be corrected to provide diagnostic image quality. Here, the authors demonstrate that a flat-panel angiography system is appropriate for scanning both legs of subjectsin vivo under weight-bearing conditions and further evaluate the three motion-correction algorithms using in vivo data. Methods: The geometry of a C-arm CT system for a horizontal scan trajectory was calibrated using the PDS-2 phantom. The authors acquired images of two healthy volunteers while lying supine on a table, standing, and squatting at several knee flexion angles. In order to identify the involuntary motion of the lower body, nine 1-mm-diameter tantalum fiducial markers were attached around the knee. The static mean marker position in 3D, a reference for motion compensation, was estimated by back-projecting detected markers in multiple projections using calibrated projection matrices and identifying the intersection points in 3D of the back-projected rays. Motion was corrected using three different methods (described in detail previously): (1) 2D projection shifting, (2) 2D deformable projection warping, and (3) 3D rigid body warping. For quantitative image quality analysis, SSIM indices for the three methods were compared using the supine data as a ground truth. Results: A 2D Euclidean distance-based metric of subjects' motion ranged from 0.85 mm (±0.49 mm) to 3.82 mm (±2.91 mm) (corresponding to 2.76 to 12.41 pixels) resulting in severe motion artifacts in 3D reconstructions. Shifting in 2D, 2D warping, and 3D warping improved the SSIM in the central slice by 20.22%, 16.83%, and 25.77% in the data with the largest motion among the five datasets (SCAN5); improvement in off-center slices was 18.94%, 29.14%, and 36.08%, respectively. Conclusions: The authors showed that C-arm CT control can be implemented for nonstandard horizontal trajectories which enabled us to scan and successfully reconstruct both legs of volunteers in weight-bearing positions. As predicted using theoretical models, the proposed motion correction methods improved image quality by reducing motion artifacts in reconstructions; 3D warping performed better than the 2D methods, especially in off-center slices. © 2014 American Association of Physicists in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.fiduci
Keywords: C-arm cone-beam CT; fiducial marker; motion artifacts; motion compensation; weight-bearing knee

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. Numerical model-based optimization - Choi, Jang-Hwan; Fahrig, Rebecca; Keil, Andreas; Besier, Thor; Pal, Saikat; McWalter, Emily; Beaupre, Gary; Maier, Andreas (Medical Physics, 2013)

Fiducial marker-based motion compensation for the acquisition of 3D knee geometry under weight-bearing conditions using a C-arm CT scanner - Choi, Jang-Hwan; Maier, Andreas; Keil, Andreas; Pal, Saikat; McWalter, Emily; Fahrig, Rebecca (Proc. 54th AAPM Annual Meeting and Technical Exhibits, 2012)

Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery - Fan, Fuxin; Kreher, Björn W.; Keil, Holger; Maier, Andreas; Huang, Yixing (Medical Physics, 2022)
Purpose: Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. Methods: In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. Results: The two methods are evaluated on simulated and real data. The direct method achieves 100% detection rates with maximal 2-pixel difference on simulated data with normal truncation and simulated data with severe noise, but fails to detect all the markers in extremely severe truncation case. The recovery method detects all the markers successfully with maximal 1 pixel difference on all simulated data sets. For real data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Conclusions: Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets. The task-specific learning is able to reconstruct structures of interest outside the FOV from severely truncated data, which has the potential to empower CBCT systems with new applications.<br
Keywords: minimally invasive spine surgery; marker detection; marker recovery; task-specific learning; truncation correction

Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, 2019)
Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will oftentimes almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can’t yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation of mitotic cells at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.936 vs. 0.829) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides.<br
Keywords: Mitotic Figure; Cell Segmentation; Digital Histopathology; Tumor Grading

Field of View Extension in Computed Tomography Using Deep Learning Prior - Huang, Yixing; Gao, Lei; Preuhs, Alexander; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen – Systeme – Anwendungen, 2020)
In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.
Keywords: Deep learning; data truncation; FOV extension; data consistency

FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales - Bergler, Christian; Gebhard, Alexander; Towers, Jared R.; Butyrev, Leonid; Sutton, Gary J.; Shaw, Tasli J. H.; Maier, Andreas; Nöth, Elmar (Scientific Reports, 2021)
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.<br

First Approaches Towards Automatic Detection of Microaneurysms in OCTA Images - Husvogt, Lennart; Alibhai, A. Yasin; Moult, Eric M.; Fujimoto, James G.; Waheed, Nadia K.; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
We investigated automatic detection of micro aneurysms in optical coherence tomo-graphy angiography. Data of two patients was gathered at the New England Eye Center. Patients with diabetic retinopathy were imaged on an Optovue Avanti device.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.firsta
Keywords: OCT; angiography; microaneurysms; automatic detection; random forest

First clinical evaluation of breathing controlled four-dimensional computed tomography imaging - Szkitsak, Juliane; Werner, René; Fernolendt, Susanne; Schwarz, Annette; Ott, Oliver; Fietkau, Rainer; Hofmann, Christian; Bert, Christoph (Physics and Imaging in Radiation Oncology, 2021)
Background and Purpose: Four-dimensional computed tomography (4DCT) has become an essential part of radiotherapy planning but is often affected by artifacts. A new breathing controlled 4DCT (i4DCT) algorithm has been introduced. This study aims to present the first clinical data and to evaluate the achieved image quality, projection data coverage and beam-on time. Material & Methods: The analysis included i4DCT data for 129 scans of patients with thoracic tumors. Projection data coverage and beam-on time were evaluated. Additionally, image quality was exemplarily discussed and rated by ten clinical experts with a 5-score-scale for 30 patients with large variations in their breathing pattern (‘challenging subgroup’). Rated images were reconstructed amplitude- and phase-based. Results: Expert scoring revealed that 78% (amplitude-based) and 63% (phase-based) of the challenging subgroup were artifact-free (rating ≥4). For the entire cohort, average beam-on time per couch position was 4.9 ± 1.6 s. For the challenging subgroup, time increased slightly but not significantly compared to the remaining patients (5.1 s vs. 4.9 s; p = 0.64). Median projection data coverage was 93% and 94% for inhalation and exhalation, respectively, for the entire cohort. The comparison for the subgroup and the remaining patients revealed a small but significant decrease of the median coverage values for the challenging cases (inhalation: 90% vs. 94%, p = 0.02; exhalation: 93% vs. 94%, p = 0.02). Conclusions: This first clinical evaluation of i4DCT shows very promising results in terms of image quality and projection data coverage. The results agree with and support the results of previous i4DCT phantom studies.
CRIS-Team Scopus Importer:2021-11-12
Keywords: 4DCT; Motion artifacts; Respiratory motion

First in-vivo Experiments with Large field-of-view Flat Panel Photon-Counting Detector - Moiz, Ahmad; Fahrig, Rebecca; Spahn, Martin; Choi, Jang-Hwan; Köster, Niko; Reitz, Silke; Hinshaw, Waldo; Pung, Leland; Moore, Teri; Maier, Andreas; Müller, Kerstin (Proceedings of the fourth international conference on image formation in x-ray computed tomography, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.firsti

First Steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain - Kunzmann, Sonja; Marzahl, Christian; Denzinger, Felix; Bertram, Christof; Klopfleisch, Robert; Breininger, Katharina; Christlein, Vincent; Maier, Andreas (Informatik aktuell, 2022)
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experimentation. Therefore, developing strategies to increase the number of annotations while lowering the needed domain knowledge is of interest. A possible strategy is the use of gamification, i.e. transforming the annotation task into a game. We propose an approach to gamify the task of annotating lung fluid cells from pathological whole slide images (WSIs). As the domain is unknown to non-expert annotators, we transform images of cells to the domain of flower images using a CycleGAN architecture. In this more assessable domain, non-expert annotators can be (t)asked to annotate different kinds of flowers in a playful setting. In order to provide a proof of concept, this work shows that the domain transfer is possible by evaluating an image classification network trained on real cell images and tested on the cell images generated by the CycleGAN network (reconstructed cell images) as well as real cell images. The classification network reaches an average accuracy of 94.73% on the original lung fluid cells and 95.25% on the transformed lung fluid cells, respectively. Our study lays the foundation for future research on gamification using CycleGANs.<br
CRIS-Team Scopus Importer:2022-05-06

Flexible Log File Parsing Using Hidden Markov Models - Kuhnert, Nadine; Maier, Andreas (2019)
We aim to model unknown file processing. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by focusing only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Second, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file processing. With changes in the raw log file distorting learned patterns, we aim the model to adapt automatically in order to maintain high quality output. After training our model on one system type, applying the model and the resulting parsing rule to a different system with slightly different log file patterns, we achieve an accuracy over 99%.<br
Keywords: Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval

Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps - Rist, Leonhard; Taubmann, Oliver; Ditt, Hendrik; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023)
Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 μm/m
Keywords: Vessel unfolding; Distortion; Cerebrovascular disease

FlexParser—The adaptive log file parser for continuous results in a changing world - Kuhnert, Nadine; Maier, Andreas (Journal of Software: Evolution and Process, 2022)
Any modern system writes events into files, called log files. Those contain crucial information which are subject to various analyses. Examples range from cybersecurity, intrusion detection over usage analyses to trouble shooting. Before data analysis is possible, desired information needs to be extracted first out of the semi-structured log messages. State-of-the-art event parsing often assumes static log events. However, any modern system is updated consistently and with updates also log file structures can change. We call those changes “mutation” and study parsing performance for different mutation cases. Latest research discovers mutations using anomaly detection post mortem, however, does not cover actual continuous parsing. Thus, we propose a novel and flexible parser, called FlexParser, which can extract desired values despite gradual changes in the log messages. It implies basic text preprocessing followed by a supervised Deep Learning method. We train a stateful LSTM on parsing one event per data set. Statefulness enforces the model to learn log message structures across several examples. Our model was tested on seven different, publicly available log file data sets and various kinds of mutations. Exhibiting an average F1-Score of 0.98, it outperforms other Deep Learning methods as well as state-of-the-art unsupervised parsers<br
Keywords: deep learning; flexible parsing; log parser; LSTM; system log

Flow Void Analysis: Multiscale Quantitation and Visualization of Choriocapillaris Alterations in Age-Related Macular Degeneration Using OCT Angiography - Alibhai, A. Yasin; Moult, Eric M.; Rebhun, Carl B.; Moreira-Neto, Carlos; Novais, Eduardo A.; Lee, Byungkun; Schottenhamml, Julia; Husvogt, Lennart; Maier, Andreas; Rosenfeld, Philip; Duker, Jay S.; Waheed, Nadia K.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : The choriocapillaris (CC) is important in the pathophysiology of age-related macular degeneration (AMD). This study aims to develop optical coherence tomography angiography (OCTA) techniques for quantifying and visualizing CC alterations. Methods : We developed a novel metric for quantifying CC flow voids (areas of no/low blood flow), as well as a display method: multiscale flow void mapping (MFVM). The metric was tested on normal controls (13 eyes, 9 subjects; 68.2±6.89 y/o), eyes with small drusen (volume<0.02 mm3; 8 eyes, 7 subjects; 69.3±11.22 y/o), large drusen (volume≥0.02 mm3; 6 eyes, 5 subjects; 80.0±5.37 y/o), nascent geographic atrophy (nGA: lesions showing hypertransmission over a linear dimension >125µm but not evident on routine fundus imaging) and/or drusen-associated geographic atrophy (DAGA) (10 eyes, 9 patients; 74.0±7.39 y/o), and geographic atrophy (GA) (6 eyes, 3 subjects; 81.7±9.40 y/o). Results : There was a trend of increasing CC alteration with age, though this was only significant (p<0.05) in controls (Fig 1A). There was also a trend of increasing CC alteration with disease state (Fig 1C): Mann-Whitney U testing (without Bonferroni correction) showed significantly (p<0.05) different metric values between nGA/DAGA and all other groups, and GA and all other groups (Fig 1D); with Bonferroni correction nGA/DAGA could not be distinguished from large drusen. This analysis did not account for inter-group age variations (Fig 1B), or multiple eyes from the same patient. MFVM was useful for visualizing CC alterations (Fig 2). Conclusions : Quantitative analysis and MVFM may be useful for monitoring CC alteration as a marker of disease progressio
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.flowvo
Keywords: oct; octa; oct angiography; amd; age-related macular degeneration

Font Augmentation: Implant and Surgical Tool Simulation for X-Ray Image Processing - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Kunze, Holger (Bildverarbeitung für die Medizin 2020, 2020)
This study investigates a novel data augmentation approach for simulating surgical instruments, tools, and implants by image composition with transformed characters, numerals, and abstract symbols from open-source fonts. We analyse its suitability for the common spatial learning tasks of multi-label segmentation and anatomical landmark detection. The proposed technique is evaluated on 38 clinical intraoperative X-ray images with a high occurrence of objects overlaying the target anatomy. We demonstrate increased robustness towards superimposed surgical objects by incorporating our technique and provide an empirical rationale about the neglectable influence of realistic object shape and intensity information.
Keywords: X-Ray; Intra-operative Imaging; Data Augmentation; Bone Segmentation; Landmark Localization

ForestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees - Rodriguez Salas, Dalia; Ravikumar, Nishant; Seuret, Mathias; Maier, Andreas (Pattern Recognition, 2020)
In this paper, we introduce a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet. Networks built using our approach are capable of handling high-dimensional data and learning representations of both visual and non-visual data. The proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes. Subsequently, such information is used to design the architecture of a sparse network, for a specific data set and application. The number of neurons is automatically adapted to the dataset. The proposed approach was evaluated using two non-visual and two visual datasets. For each dataset, 4 ensembles of randomized trees with different sizes were built. In turn, per ensemble, a sparse network architecture was designed using our approach and a fully connected network with same architecture was also constructed. The sparse networks defined using our approach consistently outperformed their respective tree ensembles, achieving statistically significant improvements in classification accuracy. While we do not beat state-of-art results with our network size and the lack of data augmentation techniques, our method exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks.<br
Keywords: Multilayer Perceptron; Random Forest; Randomized Trees; Sparse Neural Networks; Network Architecture

Form Follows Function: Smart Network Design Enables Zero-shot Network Reuse - Fu, Weilin; Husvogt, Lennart; Breininger, Katharina; Schaffert, Roman; Abu-Qamar, Omar; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2022)
In this work, we construct and train a learning-based neural network pipeline for retinal vessel segmentation on fundus images and transfer the preprocessing module directly onto OCTA en face projections. Without additional fine-tuning, the transferred module retains the edge-preserving denoising functionality, as our smart network design enables zero-shot domain adaptation. Compared to a denoising network trained on OCTA data, the transferred preprocessing module is superior with regard to performance, generalization ability, and numerical stability. In addition, Frangi’s vessel segmentation of the preprocessed images outperforms the predictions using pretrained full segmentation networks. The selection of fitting Gaussian scales of the Frangi filter in the target domain can bypass the over/under-segmentation problem caused by the vessel diameter mismatch between the two domains. The contribution of this work is two-fold: we provide an exemplary evidence that smart design of network pipelines allows for flexible functional module reuse across different imaging modalities, i.e. zero-shot domain adaptation; and we discover a deep learning-based vessel segmentation pipeline for OCTA en face images which reaches an AUC score of 0.9418 without additional training.<br
CRIS-Team Scopus Importer:2022-05-06

Fourier-based Reduction of Directed Streak Arifacts in Cone-Beam CT - Gawellek, Julia; Bier, Bastian; Gold, Garry; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fourie

Fourier Consistency-Based Motion Estimation in Rotational Angiography - Unberath, Mathias; Berger, Martin; Aichert, André; Maier, Andreas (Proc. Bildverarbeitung für die Medizin (BVM) 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fourie

Frangi-Net: A Neural Network Approach to Vessel Segmentation - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Würfl, Tobias; Fujimoto, James G.; Moult, Eric M.; Maier, Andreas (BildVerarbeitung für die Medizin (BVM) 2018, 2018)
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net”), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 scor
Keywords: Vessel segmentation; Precision learning; Frangi-Net; Fundus imaging

Free-breathing fat and R2∗ quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction - Schneider, Manuel; Benkert, Thomas; Solomon, Eddy; Nickel, Dominik; Fenchel, Matthias; Kiefer, Berthold; Maier, Andreas; Chandarana, Hersh; Block, Kai Tobias (Magnetic Resonance in Medicine, 2020)
Purpose: To develop a free-breathing hepatic fat and (Formula presented.) quantification method by extending a previously described stack-of-stars model-based fat-water separation technique with additional modeling of the transverse relaxation rate (Formula presented.) . Methods: The proposed technique combines motion-robust radial sampling using a stack-of-stars bipolar multi-echo 3D GRE acquisition with iterative model-based fat-water separation. Parallel-Imaging and Compressed-Sensing principles are incorporated through modeling of the coil-sensitivity profiles and enforcement of total-variation (TV) sparsity on estimated water, fat, and (Formula presented.) parameter maps. Water and fat signals are used to estimate the confounder-corrected proton-density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion-averaged and motion-resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and (Formula presented.) parameter maps were compared to a breath-holding Cartesian reference approach. Results: Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪.01) correlations between radial and Cartesian PDFF measurements for both the motion-averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion-resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion-averaged technique overestimated hepatic (Formula presented.) values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory-resolved reconstruction led to better (Formula presented.) value consistency (slope: 0.77; intercept: 7.5 1/s). Conclusions: The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and (Formula presented.) quantification in patients with limited breath-holding capabilities. For accurate (Formula presented.) estimation, respiratory-resolved reconstruction should be used.
CRIS-Team Scopus Importer:2020-04-28
Keywords: compressed sensing; free-breathing fat/ R2∗ quantification; multi-echo 3D stack-of-stars GRE; nonalcoholic fatty liver disease; radial sampling; respiratory motion-resolved reconstruction

Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing - Hoppe, Elisabeth; Wetzl, Jens; Forman, Christoph; Koerzdoerfer, Gregor; Schneider, Manuel; Speier, Peter; Schmidt, Michaela; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)
We present a free-breathing multi-contrast 3-D cardiac CINE acquisition and reconstruction technique based on Compressed Sensing. Inversion pulses were repeatedly applied during a continuous acquisition to sample contrast- and cardiac-resolved 3-D data, while a self-navigation method was applied for respiratory gating. Validation was performed in a phantom, showing recovery curves and T1* maps in good correlation to known T1 values for the phantom as well as a MOLLI reference measurement. Feasibility for in-vivo application was demonstrated in a healthy voluntee
Keywords: Magnetic Resonance Imaging; Cardiac CINE Magnetic Resonance Imaging; Multicontrast Imaging; Compressed Sensing

Free-Breathing Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart using Adaptive Triggering and Retrospective Gating - Wetzl, Jens; Lugauer, Felix; Kroeker, Randall; Schmidt, Michaela; Maier, Andreas; Forman, Christoph (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.freebr_8

Free-Breathing, Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart Using Cartesian Sampling - Wetzl, Jens; Lugauer, Felix; Schmidt, Michaela; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.freebr

Free-Breathing Whole-Heart Coronary MRA: Motion Compensation Integrated into 3D Cartesian Compressed Sensing Reconstruction - Forman, Christoph; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Zenge, Michael O. (MICCAI 2013, Part II, LNCS 8150, 2013)

Frist experimental results on long-object imaging using a reverse helical trajectory with a C-arm system - Yu, Zhicong; Maier, Andreas; Schönborn, Manfred; Vogt, Florian; Köhler, Christoph; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (Proceedings of The second international conference on image formation in x-ray computed tomography, 2012)

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks - Vesal, Sulaiman; Maier, Andreas; Ravikumar, Nishant (Journal of Imaging, 2020)
Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.<br
Keywords: cardiac MRI; deep neural network; CNN; multistage segmentation; MRI segmentation; cardiovascular diseases

Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images - Yoon, Seung Su; Fischer, Carola; Amsel, Daniel; Monzon, Maria; Toupin, Solenn; Pezel, Théo; Garot, Jérôme; Wetzl, Jens; Maier, Andreas; Giese, Daniel; Giese, Daniel (European Journal of Radiology, 2023)
Purpose: In cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). Method: Two sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. Results: The accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. Conclusion: The results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters.
CRIS-Team Scopus Importer:2023-08-11
Keywords: AI; CMR; Longitudinal shortening; Mitral valve motion; Tricuspid valve motion

Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps - Sanders, James Chester; Ritt, Philipp; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (IEEE Transactions on Medical Imaging, 2016)
We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 ± 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.
EVALuna2:13863

Fully automated Machine Learning-based selection of optimal bSSFP frequency offset for artifact reduction in cardiac MRI - Yoon, Seung Su; Gadjimuradov, Fasil; Schmidt, Michaela; Wetzl, Jens; Maier, Andreas (Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A Joint Summit of the EACVI and SCMR, 2022)

Fully automatic AI-based extraction of mitral valve motion parameters on long axis CINE images – validation and application on N=2000 patient datasets - Yoon, Seung Su; Fischer, Carola; Toupin, Solenn; Pezel, Théo; Garot, Jérome; Wetzl, Jens; Maier, Andreas; Giese, Daniel (Society for Cardiovascular Magnetic Resonance 25th Annual Scientific Sessions, 2022)

Fully automatic AI-based valve motion parameter extraction on long axis CINE images – application on N=11000 patient datasets - Yoon, Seung Su; Fischer, Carola; Toupin, Solenn; Pezel, Théo; Garot, Jérome; Wetzl, Jens; Maier, Andreas; Giese, Daniel (Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A Joint Summit of the EACVI and SCMR, 2022)

Fully Automatic Assessment of Speech of Children with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Batliner, Anton; Nkenke, Emeka; Schuster, Maria (Informatica, 2006)

Fully-automatic CT data preparation for interventional X-ray skin dose simulation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Stimpel, Bernhard; Syben, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen, 2020)
Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e.,g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projection
Keywords: X-ray simulation; Computed tomography; Data preparation

Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning - Monzon, Maria; Yoon, Seung Su; Fischer, Carola; Maier, Andreas; Wetzl, Jens; Giese, Daniel (International Society for Magnetic Resonance in Medicine (ISMRM) 29th Annual Meeting & Exhibition, 2021)

Fully Automatic Head Motion Correction for Interventional C-arm Systems using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Datta, Sanjit; Gehrisch, Sonja; Moore, Teri; Marks, Michael P; Maier, Andreas; Fahrig, Rebecca (Proceedings of the 13th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.fullya_7

Fully Automatic Scar Segmentation on LGE Images with Reduced Contrast Agent Dose - Vornehm, Marc; Spiske, Ute; Fenski, Maximilian; Wetzl, Jens; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Giese, Daniel (Proceedings of the SCMR 26th Annual Scientific Sessions, 2023)

Fully automatic segmentation of left ventricular anatomy in 3-DLGE-MRI - Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander (Computerized Medical Imaging and Graphics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fullya_0

Fully Automatic Segmentation of Papillary Muscles in 3-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.fullya_7

Fully automatic separation of three capillary plexuses in the macular region - Schottenhamml, Julia; Ploner, Stefan; Moult, Eric; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020)
CRIS-Team WoS Importer:2020-09-11

Fusing Motion Estimates for CBCT Reconstruction - Bier, Bastian; Unberath, Mathias; Geimer, Tobias; Ravikumar, Nishant; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fusing

GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection - El-Ghoussani, Amir; Rodriguez Salas, Dalia; Seuret, Mathias; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection - El-Ghoussani, Amir; Rodriguez Salas, Dalia; Seuret, Mathias; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Mammography is an important part of breast cancer diagnostics, as it allows to inspect the inner breast structure without physically penetrating breast tissue. Commonly, mammograms tend to vary in their visual appearance based on the specific device and the circumstances under which the mammogram is acquired. Such images could cause artificial intelligence algorithms to fail as they can introduce an undesired variation into the data. This study intends to put these images to use by utilizing a cycle-consistent Generative Adversarial Network (GAN) in order to augment the training data by diversifying instances of each visual domain into all the available ones. A publicly available dataset was augmented to train a detection network; the GANs used for the augmentation were trained with an in-house dataset with three visually different domains.Results show that using our augmentation technique consistently increases the detection performance by reaching a mean average precision of up to 0.82 against 0.77 without augmenting the data.<br

GAN Generated Model Observer for one Class Detection in SPECT Imaging - Rao, Disha; Reymann, Maximilian; Faley, Patrick; Massanes, Francesc; Gohn, Wesley; Vija, A. Hans (GAN Generated Model Observer for one Class Detection in SPECT Imaging, 2021)
Model observers are mathematical models used to perform a specific task, such as lesion detection. In this document, we will restrict ourselves to ideal model observers, which do not try to mimic human performance but try to perform perfect classification. However, we will not be following the usual definition of ideal model observer, which describes the model observer as a statistical classifier between two classes. Instead we will define a GAN network and train it to generate images from class 𝐻0, without lesions, and then use the discriminator of the GAN network as a model observer. Our method relies on pix2pix, which is a type of conditional GAN, the network is first trained to generate SPECT reconstructions-like data from the corresponding CT images. Later, the discriminator is used on simulated lesions to validate is usage as a classifier.<br
Keywords: Model Observer; SPECT; Deep Learning; GAN

Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems - Packhäuser, Kai; Folle, Lukas; Thamm, Florian; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)

Generation of Personalized Computational Phantoms Using Only Patient Metadata - Zhong, Xia; Strobel, Norbert; Sanders, James Chester; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_4

Generation of Synthetic 3D Data Using Simulated MR Examinations in Augmented Reality - Serra Juhé, Aniol; Rinck, Daniel; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
In the future, medical imaging devices such as computed tomography (CT) and magnetic resonance (MR) are expected to become increasingly autonomous. Therefore, design criteria and workflow of such devices will change substantially. Moreover, sensor data of the system are required to develop scene understanding algorithms to support and guide the user. In this work, we present an approach to generate synthetic 3D point cloud data from a simulated MR examination experienced on the Microsoft Hololens2. The complete workflow of an MR examination using a virtual, autonomous MR scanner is reproduced in an AR scene. The user is recorded by a system of active stereo vision RGBD-cameras while interacting with AR elements. A registration routine of the AR scene and the RGBD-cameras is described, and accuracy measurements are provided. The real point clouds are fused with virtually generated point clouds from the AR scene.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Augmented Reality; Synthetic data; Workflow Analysis

Generation of synthetic Image Data for the Evaluation of Brain Shift Compensation Methods - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_1

Geometric Adjustment of X-ray Tomosynthesis - Grulich, Tobias; Holub, Wolfgang; Haßler, Ulf; Aichert, André; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.geomet

Geometrical jitter correction in computed tomography - Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the third international conference on image formation in X-ray computed tomography, 2014)
UnivIS-Import:2018-09-06:Pub.2014.tech.IMMD.IMMD5.geomet_0

Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography - Wagner, Fabian; Thies, Mareike; Maul, Noah; Pfaff, Laura; Aust, Oliver; Pechmann, Sabrina; Syben, Christopher; Maier, Andreas (17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2023)
The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce radiation exposure and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization. By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry. Our experiments on real X-ray microscope (XRM) tomographic mouse tibia bone scans show that our method improves reconstructions by 3.1-7.4%/7.7-17.6% in terms of PSNR/SSIM with respect to the interpolation baseline. Our approach is applicable as a flexible self-supervised projection inpainting tool for tomographic applications.<br

Geometric modeling of the aortic inner and outer vessel wall from CTA for aortic dissection analysis - Eigen, Katharina; Maier, Andreas; Dohle, Daniel-Sebastian; Sühling, Michael; Wels, Michael (Proc. SPIE, 2018)
In this paper, we present a novel method for modeling both layers of the aortic walls in cases of aortic dissections for analysis from Computed Tomography Angiography. It involves a fast initialization of the associated physiological and pathological lumina and further editing on non-linearly formatted and cross-sectional views. Fast and accurate derivation of 3D models of these inner and outer vessel walls is crucial to analyze true and false lumen, to accelerate processing times in research studies, and to answer therapy questions. Since the aorta is a relatively large vessel, our system makes use of a point-based surface interpolation with compactly supported radial basis functions requiring only few surface constraints. Where possible, we use a semi-Automatic approach to segment the vessel walls using an Active Contour Model, which detects the contours in the vessel's cross-sectional planes, stating the constraints for interpolation. After initialization, editing on non-linearly formatted and crosssectional views is possible due to handling user input through tangent frame bundles to dismiss contradictory surface samples before updating the models with the new constraints. Our proposed method was evaluated in a user study to measure processing times and achievable model accuracy with respect to an expert-defined ground truth. The users needed 19 minutes on average to derive one model (both walls) and attained a mean surface distance of about 1.0 mm for the outer vessel wall, respectively, 1.6 mm for the inner wall. Using our method instead of open source program for geometric modeling saves 26 minutes per dataset.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.geomet_0
Keywords: Active Contour Model; Aortic Dissection; Tangent Frame Bundles

Geometric primitive refinement for structured light cameras - Fürsattel, Peter; Placht, Simon; Maier, Andreas; Riess, Christian (Machine Vision and Applications, 2018)
Keywords: Geometric primitives; Structured light; Range imaging

Glacier Calving Front Segmentation Using Attention U-Net - Holzmann, Michael; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (2021 IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 2021)
An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends. Previous studies have proposed deep learning-based methods to semi-automatically delineate the calving fronts of tidewater glaciers. They used U-Net to segment the ice and non-ice regions and extracted the calving fronts in a post-processing step. In this work, we show a method to segment the glacier calving fronts from SAR images in an end-to-end fashion using Attention U-Net. The main objective is to investigate the attention mechanism in this application. Adding attention modules to the state-of-the-art U - N et network lets us analyze the learning process by extracting its attention maps. We use these maps as a tool to search for proper hyperparameters and loss functions in order to generate higher qualitative results. Our proposed attention U-Net performs comparably to the standard U-Net while providing additional insight into those regions on which the network learned to focus more. In the best case, the attention U-Net achieves a 1.5 % better Dice score compared to the canonical U-Net with a glacier front line prediction certainty of up to 237.12 meters.

Glaucoma classification in 3 x 3 mm en face macular scans using deep learning in a different plexus - Schottenhamml, Julia; Wuerfl, Tobias; Mardin, Sophia; Ploner, Stefan; Husvogt, Lennart; Hohberger, Bettina; Lämmer, Robert; Mardin, Christian; Maier, Andreas (Biomedical Optics Express, 2021)
Glaucoma is among the leading causes of irreversible blindness worldwide. If diagnosed and treated early enough, the disease progression can be stopped or slowed down. Therefore, it would be very valuable to detect early stages of glaucoma, which are mostly asymptomatic, by broad screening. This study examines different computational features that can be automatically deduced from images and their performance on the classification task of differentiating glaucoma patients and healthy controls. Data used for this study are 3 x 3 mm en face optical coherence tomography angiography (OCTA) images of different retinal projections (of the whole retina, the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP) and the deep capillary plexus (DCP)) centered around the fovea. Our results show quantitatively that the automatically extracted features from convolutional neural networks (CNNs) perform similarly well or better than handcrafted ones when used to distinguish glaucoma patients from healthy controls. On the whole retina projection and the SVP projection, CNNs outperform the handcrafted features presented in the literature. Area under receiver operating characteristics (AUROC) on the SVP projection is 0.967, which is comparable to the best reported values in the literature. This is achieved despite using the small 3 x 3 mm field of view, which has been reported as disadvantageous for handcrafted vessel density features in previous works. A detailed analysis of our CNN method, using attention maps, suggests that this pertbrmance increase can be partially explained by the CNN automatically relying more on areas of higher relevance for feature extraction. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
CRIS-Team WoS Importer:2021-12-17

GMM-based Synthetic Samples for Classification of Hyperspectral Images with Limited Training Data - Davari, Amirabbas; Aptoula, Erchan; Yanikoglu, Berrin; Maier, Andreas; Riess, Christian (IEEE Geoscience and Remote Sensing Letters, 2018)
Keywords: hyperspectral remote sensing image classification; limited training data; synthetic data; EMAP

GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification - Davari, Amirabbas; Christlein, Vincent; Vesal, Sulaiman; Maier, Andreas; Riess, Christian (Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns Part II, 2017)

GPU-accelerated SART reconstruction using the CUDA programming environment - Keck, Benjamin; Hofmann, Hannes; Scherl, Hermann; Kowarschik, Markus; Hornegger, Joachim (Medical Imaging 2009: Physics of Medical Imaging, 2009)
The Common Unified Device Architecture (CUDA) introduced in 2007 by NVIDIA is a recent programming model making use of the unified shader design of the most recent graphics processing units (GPUs). The programming interface allows algorithm implementation using standard C language along with a few extensions without any knowledge about graphics programming using OpenGL, DirectX, and shading languages. We apply this novel technology to the Simultaneous Algebraic Reconstruction Technique (SART), which is an advanced iterative image reconstruction method in cone-beam CT. So far, the computational complexity of this algorithm has prohibited its use in most medical applications. However, since today's GPUs provide a high level of parallelism and are highly cost-efficient processors, they are predestinated for performing the iterative reconstruction according to medical requirements. In this paper we present an efficient implementation of the most time-consuming parts of the iterative reconstruction algorithm: forward- and back-projection. We also explain the required strategy to parallelize the algorithm for the CUDA 1.1 and CUDA 2.0 architecture. Furthermore, our implementation introduces an acceleration technique for the reconstruction compared to a standard SART implementation on the GPU using CUDA. Thus, we present an implementation that can be used in a time-critical clinical environment. Finally, we compare our results to the current applications on multi-core workstations, with respect to both reconstruction speed and (dis-)advantages. Our implementation exhibits a speed-up of more than 64 compared to a state-of-the-art CPU using hardware-accelerated texture interpolation. © 2009 SPIE.

GPU Accelerated Time-of-Flight Super-Resolution for Image-Guided Surgery - Wetzl, Jens; Taubmann, Oliver; Haase, Sven; Köhler, Thomas; Kraus, Martin; Hornegger, Joachim (Bildverarbeitung für die Medizin, 2013)

GPU Denoising for Computed Tomography - Maier, Andreas; Fahrig, Rebecca (Graphics Processing Unit-Based High Performance Computing in Radiation Therapy, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.gpuden
Keywords: noise; denoising; GPU

Gradient-based geometry learning for fan-beam CT reconstruction - Thies, Mareike; Wagner, Fabian; Maul, Noah; Folle, Lukas; Meier, Manuela; Rohleder, Maximilian; Schneider, Linda-Sophie; Pfaff, Laura; Gu, Mingxuan; Utz, Jonas; Denzinger, Felix; Manhart, Michael; et al. (Physics in Medicine and Biology, 2023)
Objective. Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. Approach. The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone. Main results. The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742. Significance. Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep model
Keywords: computed tomography; projective geometry; differentiable programming; motion compensation

Gradient-Delay-Compensated Radial MRI for Fat Content and Fatty Acid Composition Quantification - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the ISMRM Workshop on Quantitative Body Imaging, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.gradie_1

Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps - Meister, Felix; Passerini, Tiziano; Audigier, Chloé; Lluch, Èric; Mihalef, Viorel; Ashikaga, Hiroshi; Maier, Andreas; Halperin, Henry; Mansi, Tommaso (Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges, 2021)
Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. The predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. The mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). The results show that the proposed method, trained on synthetically generated data, may generalize to real data.<br
Keywords: Cardiac computational modeling; Deep learning; Electroanatomical contact mapping

Graph Neural Networks in Multi-Stained Pathological Imaging: Extended Comparative Analysis of Radiomic Features - Rivera Monroy, Luis; Rist, Leonhard; Ostalecki, Christian; Bauer, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (2025)
Purpose: This study investigates Radiomics features for Graph Neural Networks (GNNs) in MELC pathology sample classification focusing on often misdiagnosed skin diseases. Methods: GNNs processing multiple pathological slides together as cell-level graphs are compared to XGBoost and Random Forest. The analysis assesses the use of MELC vs. Radiomics features, their dimensionality reduction with UMAP or tSNE and the graph connectivity based on spatial and feature closeness. Results: Integrating Radiomics features in a spatially connected graph markedly outperforms standard models when classifying pathologically similar diseases. Additionally, the UMAP dimensionality reduction techniques improves GNN classification performance. Conclusion: Radiomics, processed with GNNs, shows promise for multi-disease classification, enhancing diagnosis accuracy. Considering the potential, future research should extend these methods to a broader range of disease
https://cris.fau.de/converis/publicweb/Publication/321533326
Keywords: Pathology; Dermatology; Artificial Intelligence and Machine Learning; Radiomics; Multi-array Imaging; Graph Neural Network; MELC; Histopathological Analysis

Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring - Durlak, Felix; Wels, Michael; Schwemmer, Chris; Sühling, Michael; Steidl, Stefan; Maier, Andreas (Machine Learning in Medical Imaging, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.growin

Guest editorial of the IJCARS—BVM 2018 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2019)
CRIS-Team Scopus Importer:2019-08-06

Guest editorial of the IJCARS MICCAI 2016 special issue - Ourselin, Sebastien; Sabuncu, Mert R.; Wells, William; Joskowicz, Leo; Unal, Gozde; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2017)
CRIS-Team Scopus Importer:2022-06-05

Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors - Manhart, Michael; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas (Proceedings of the Third CT Meeting, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_6

Guided Noise Reduction with Streak Removal for High Speed Flat Detector CT Perfusion - Manhart, Michael; Aichert, André; Kowarschik, Markus; Deuerling-Zheng, Yu; Struffert, Tobias; Dörfler, Arnd; Maier, Andreas; Hornegger, Joachim (Proceedings NSS/MIC 2013, 2013)
Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (flat detector CT (FD-CT)) is a novel technique with high potential benefit for catheter-guided treatment of stroke in the interventional suite. New high speed protocols (HSP) with increased C-arm rotation speed enable fast acquisitions of FD-CT volumes and allow for sampling the contrast flow with improved temporal resolution. However, the peak contrast attenuation values of brain tissue typically lie in a range of 5-30 HU. Thus perfusion imaging is very sensitive to noise. Recently we introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by denoising in volume space using joint bilateral filtering (JBF). In the evaluation FDK-JBF achieved comparable results to algebraic techniques, but is computationally less costly. Yet the angular sampling of the projection data in the HSP is coarse, which leads to streak artifacts in the reconstructed volumes. Mask volumes are subtracted from the contrast agent enhanced (bolus) volumes and the streak artifacts are subtracted out if the patient does not move during the acquisition. However, in case of motion the streak artifacts will not be identical in the mask and bolus volumes. We show that these streaks can lead to severe artifacts in the perfusion maps and describe a novel technique for streak removal (SR), which is based on streak detection by using time-contrast curve analysis.We evaluated the FDK-SR-JBF algorithm in a phantom and a patient study and show that noise and streaks can be reduced within a short computation time. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.guided_5
Keywords: Flat detector CT; noise reduction; Perfusion imaging; stroke treatment

Handling Label Uncertainty on the Example of Automatic Detection of Shepherd’s Crook RCA in Coronary CT Angiography - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Kordon, Florian Johannes; Wagner, Fabian; Mehltretter, Stephanie; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buss, Sebastian J.; Görich, Johannes; Sühling, Michael; et al. (Proceedings - International Symposium on Biomedical Imaging, 2023)
Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd’s Crook (SC) Right Coronary Artery (RCA) – an anatomical norm variant of the coronary vasculature – the complexity of this procedure is increased. Automated reporting of this variant from coronary CT angiography screening would ease prior risk assessment. We propose a 1D convolutional neural network which leverages a sequence of residual dilated convolutions to automatically determine this norm variant from a prior extracted vessel centerline. As the SC RCA is not clearly defined with respect to concrete measurements, labeling also includes qualitative aspects. Therefore, 4.23 % samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs, with 5.97 % being labeled as sure SC RCAs. We explore measures to handle this label uncertainty, namely global/model-wise random assignment, exclusion, and soft label assignment. Furthermore, we evaluate how this uncertainty can be leveraged for the determination of a rejection class. With our best configuration, we reach an area under the receiver operating characteristic curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of up to 0.020 AUC when rejecting 10 % of the data and leveraging the labeling uncertainty information in the exclusion process.<br
CRIS-Team Scopus Importer:2023-10-06
Keywords: Coronary CT Angiography; Label Uncertainty; Shepherd's Crook RCA

Handwriting Analysis with Focus on Writer Identification and Writer Retrieval - Christlein, Vincent (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
In the course of the mass digitization of historical as well as contemporary sources, an individual examination by means of historical or forensic experts is no longer feasible. A solution could be an automatic handwriting analysis that determines or suggests script attributes, such as the writer or the date of a document. In this work, several novel techniques based on machine learning are presented to obtain these attributes from a single document image. The focus lies on writer recognition for which a novel pipeline is developed, which identifies the correct writer of a given sample in over 99% of all tested contemporary datasets, numbering between 150 and 310 writers each, with four to five samples per writer. In a large historical dataset, consisting of 720 writers and five samples per writer, an identification rate of close to 90% is achieved. Robust local descriptors play a major role in the success of this pipeline. Shape- and histogram-based descriptors prove to be very effective. Furthermore, novel deep-learning-based features are developed using deep convolutional neural networks, which are trained with writer information from the training set. While these features achieve very good results in contemporary data, they lack distinctiveness in the evaluated historical dataset. Therefore, a novel feature learning technique is presented that solves this by learning robust writer-independent script features in an unsupervised manner. The computation of a global descriptor from the local descriptors is the next step. For this encoding procedure, various techniques from the speech and computer vision community are investigated and thoroughly evaluated. It is important to counter several effects, such as feature correlation and the over-counting of local descriptors. Overall, methods based on aggregating first order statistics of residuals are the most effective approaches. Common writer recognition methods use the global descriptors directly for comparison. In contrast, exemplar classifiers are introduced in this thesis allowing sample-individual similarities to be learned, which are shown to be very effective for an improved writer recognition. This writer recognition pipeline is adapted to other tasks related to digital paleography. Medieval papal charters are automatically dated up to an error range of 17 years. Fur- thermore, an adapted pipeline is among the best to classify medieval Latin manuscripts into twelve different script types. This information can then be used for a pre-sorting of documents or as a preprocessing step for handwritten text recognition. It turns out that counteracting different illumination and contrast effects is an important factor for deep-learning-based approaches. The observation that script has similar tubal structures to blood vessels is exploited for an improved text block segmentation in historical data by means of a well-known medical filtering technique. This work sets new recognition standards for several tasks, allowing the automatic document analysis of large corpora with low error rates. These methods are also applicable to other fields, such as forensics or paleography, to determine writers, script types or other metadata of contemporary or historical document
Keywords: handwriting analysis; writer identification; writer retrieval

Handwritten Text Recognition Error Rate Reduction in Historical Documents using Naive Transcribers - Christlein, Vincent; Nicolaou, Anguelos; Schlauwitz, Thorsten; Späth, Sabrina; Herbers, Klaus; Maier, Andreas (2018)
Handwritten text recognition (HTR) is a difficult research problem. In particular for historical documents, this task is hard as handwriting style, orthography, and text quality pose significant challenges. Creation of a single multi-purpose HTR system seems to be out of reach for current state-of-the-art systems. Therefore, we are interested in fast creation of specialized HTR systems for a particular set of historical documents. Still manual annotation by historical experts is expensive and can often not be applied at a large scale. Instead, we use the transcripts of naive transcribers that may still contain a significant amount of errors. In this paper, we propose to fuse the recognized word-chain with naive transcribers that can be obtained in a cost-effective way. For the actual fusion, we rely on a word-level approach, the so-called Recognizer Output Voting Error Reduction (ROVER). Results indicate that we are able to reduce the Word Error Rate (WER) of an HTR system trained with only few pages from 2.6 % to 19.2% with two additional transcribers with 25.1% and 27.1% WER each. This performance is already close to current state-of-the-art systems trained with significantly more dat
Keywords: handwritten text recognition; line fusion; naive transcribers; Nürnberg; Briefbücher; 15. Jahrhundert

Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware - Rücker, Nadine; Pflueger, Lea; Maier, Andreas (Journal of Digital Imaging, 2021)
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil’s condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662–1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further.
CRIS-Team Scopus Importer:2021-01-15
Keywords: Gaussian process regression; Hardware failure prediction; Machine learning; Time series classification

Hardware-unabhängige Beschleunigung von Medizinischer Bildverarbeitung mit OpenCL - Siegl, Christian; Hofmann, Hannes; Keck, Benjamin; Prümmer, Marcus; Hornegger, Joachim (Bildverarbeitung für die Medizin 2011, 2011)

HDPA: historical document processing and analysis framework - Lenc, Ladislav; Martínek, Jiří; Král, Pavel; Nicolao, Anguelos; Christlein, Vincent (Evolving Systems, 2021)
Nowadays, the accessibility of digitized historical documents is extremely important to facilitate fast and efficient retrieval of historical information and knowledge extraction from such data. To provide such functionality, it is necessary to convert document images into plain text using optical character recognition (OCR). Many OCR related methods and tools have been proposed, however, they are often too complicated for a standard user, some important parts are missing or they are not available in free versions. Therefore, this paper describes a complex and flexible web framework for historical document manipulation and analysis with the main focus on OCR. The framework contains eight modules to facilitate three main tasks: image pre-processing and segmentation, creation of data for OCR model training and the OCR itself. This framework is freely available for non commercial purposes. We have experimentally evaluated this framework on real data and we have shown that this system is efficient and can save human labour in the process of annotated data preparation. Moreover, we have reached state-of-the-art OCR results.<br
Keywords: CNN; Document analysis; Framework; Historical documents; LSTM; Neural networks; OCR

Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble - Chatterjee, Satyaki; Ramachandran, Adithya; Neergaard, Thorkil F.B.; Maier, Andreas; Bayer, Siming (NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022)
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417 kW for RMSE is achieved with the proposed framework in comparison to all other methods.<br
Keywords: CNN; District Heating; Time Serie Forecasting

Helfen "Fallen" bei verrauschten Daten? - Spracherkennung mit TRAPs - Maier, Andreas; Hacker, Christian; Steidl, Stefan; Nöth, Elmar (Fortschritte der Akustik: Plenarvorträge und Fachbeiträge der 31. Deutschen Jahrestagung für Akustik DAGA 2005, München, 2005)

Helical Dark-field Fiber Reconstruction - Felsner, Lina; Syben-Leisner, Christopher; Maier, Andreas; Riess, Christian (International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)

High-resolution dynamic CE-MRA of the thorax enabled by iterative TWIST reconstruction - Wetzl, Jens; Forman, Christoph; Wintersperger, Bernd J.; D'Errico, Luigia; Schmidt, Michaela; Mailhé, Boris; Maier, Andreas; Stalder, Aurélien F. (Magnetic Resonance in Medicine, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.highre
Keywords: dynamic contrast-enhanced magnetic resonance angiography; iterative reconstruction; TWIST

High Resolution Iterative CT Reconstruction using Graphics Hardware - Keck, Benjamin; Hofmann, Hannes; Scherl, Holger; Kowarschik, Markus; Hornegger, Joachim (2009 IEEE Nuclear Science Symposium Conference Record, 2009)

High-speed slot-scanning radiography using small-angle tomosynthesis: Investigation of spatial resolution - Luckner, Christoph; Herbst, Magdalena; Weber, Thomas; Beister, Marcel; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Medical Physics, 2019)
Purpose This paper studies spatial resolution that is achievable with a fast slot-scanning tomosynthesis approach for orthopedic examinations. Hereby, we use parallel scanning motion implemented in a twin robotic x-ray system. Methods We have measured and analyzed the modulation transfer function (MTF) for various combinations of scanning speed, x-ray tube voltage, pulse length, the nominal focal spot size as well as source-to-object distances. Moreover, we present a theoretical model which describes the system in terms of the MTF. The system was equipped with newly developed linear trajectory prototypes for slot scanning. The acquired images form the basis for a small-angle tomosynthesis reconstruction. In total, three different scanning speeds (27, 14, 8 cm/s), pulse lengths (1, 2, 4 ms), tube voltages (80, 100, 120 kV), two nominal focal spot sizes (0.6, 1.0), and three source-to-object distances (950, 1050, 1150 mm) were investigated. To determine the resolution capabilities, we measured the MTF for the given parameter space. The results were then used to design a filter that yields a desired resolution in the reconstructed image. In addition, we also measured the noise power spectrum (NPS) to show the influence of the aforementioned filters on the noise distribution. Results We have shown that the presented model is in good agreement with the performed measurements. Scanning speed and pulse width have an impact on the MTF in the scanning direction. Up to a travel distance of 0.3 mm during an x-ray pulse, an isotropic resolution can be achieved. Longer pulse width or higher scanning speed cause anisotropic resolution. Moreover, it is shown that none of the investigated parameters have an influence on the MTF perpendicular to the scanning direction (slot direction). The 10% MTF value ranges between 9 and 18 lp/cm in the scanning direction and about 18 lp/cm in slot direction. Tube voltage, nominal focal spot size as well as the source-to-object distance showed no major impact on the system MTF. In terms of the anisotropic resolution capabilities, we have shown that limiting the resolution in the slot direction to obtain isotropic resolution is possible yet at the cost of an inhomogeneous noise pattern. However, maintaining the resolution in slot direction will provide a better edge response and a more homogeneous noise texture at the cost of an inhomogeneous image resolution. Conclusion We have demonstrated the feasibility of the slot-scanning technique using a twin-robotic x-ray system. Even the fastest scanning mode (27 cm/s) yields image resolution on a level that is sufficient for typical orthopedic examinations in terms of musculoskeletal (MSK) measurements. Moreover, it could be shown that the application of specifically designed target MTFs on two-dimensional x-ray images is feasible.
CRIS-Team WoS Importer:2019-11-05

High-speed video endoscopy to improve the diagnosis of voice disorders - Dürr, Stephan; Schützenberger, Anne; Kist, Andreas; Semmler, Marion; Schraut, Tobias; Arias Vergara, Tomás; Döllinger, Michael (2022)
Introduction A voice disorder causes a significant loss of quality of life. Today, stroboscopy is the standard diagnostic method. Causes are incomplete glottis closure and/or irregular, aperiodic vocal fold oscillations, which can only be made visible to a limited extent by stroboscopy. Methods High-speed video endoscopy (HSV) is a new examination method that significantly improves the temporal resolution and thus allows a clearer assessment of vocal fold vibrations. Current, clinically applicable HSV systems allow a temporal representation of 4000-8000 images per second with sufficiently good spatial resolution. Using image processing, it is possible to calculate quantitative parameters regarding the vocal fold vibration characteristics (i.e. glottis closure, periodicity, symmetry). Results The presented high-speed system (OpenHSV) in combination with the analysis software "Glottis Analysis Tools" (GAT) allows both the visualisation and the quantification of the disturbance of vocal fold vibrations. The HSV thus provides objective support in making a diagnosis and assessing the course of therapy. Conclusions HSV is a new innovative method that allows quantitative analysis and thus better diagnosis of voice disorders. For example, both phonatory vocal fold arrest, which is crucial for distinguishing between simple leukoplakia or carcinoma in situ or carcinoma, and irregular oscillations in functional dysphonia can be assessed more accurately and appropriate therapy can be initiate

Home Assistance System for Elderly People - Ott, Stefan; Spiegl, Werner; Soutschek, Stefan; Maier, Andreas; Steidl, Stefan; Nöth, Elmar (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images - Shetty, Karthik; Birkhold, Annette; Egger, Bernhard; Jaganathan, Srikrishna; Strobel, Norbert; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
We propose a method to address the challenge of generating a 3D digital twin of a patient during an X-ray guided medical procedure from a single 2D X-ray projection image, a problem that is inherently ill-posed. To tackle this issue, we aim to infer the parameters of Bones, Organs and Skin Shape (BOSS) model, a deformable human shape and pose model. There are currently two main approaches for model-based estimation. Optimization-based methods try to iteratively fit a body model to 2D measurements, they produce accurate 2D alignments but are slow and sensitive to initialization. On the other hand, regression-based methods use neural networks to estimate the model parameters directly, resulting in faster predictions but often with misalignments. Our approach combines the benefits of both techniques by implementing a fully differentiable paradigm through the use of higher-order optimizers that only require the Jacobian, which can be determined implicitly. The network was trained on synthetic CT and real CBCT image data, ensuring view independence. We demonstrate the potential clinical applicability of our method by validating it on multiple datasets covering diverse anatomical regions, and achieving an error of 27.98 mm.
CRIS-Team Scopus Importer:2023-12-15

How Big is Big Enough?: A Large-Scale Histological Dataset of Mitotic Figures - Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Bildverarbeitung für die Medizin 2020, 2020)
Quantification of mitotic figures (MF) within the tumor areas of highest mi- totic density is the most important prognostic parameter for outcome assessment of many tumor types. However, high intra- and inter-rater variability results from difficulties in individual MF identification and region of interest (ROI) se- lection due to uneven MF distribution. Deep learning-based algorithms for MF detection and ROI selection are very promising methods to overcome these lim- itations. As of today, few datasets of human mammary carcinoma are available. They provide labels only in small image sections of the whole slide image (WSI) and include up to 1,552 MF annotations. Our research group has developed a large-scale, open access dataset with annotations for MF in 32 cases of canine cutaneous mast cell tumors. Entire WSI were completely labeled by two pathologists resulting in 44,800 MF anno- tations. Of those, 5.5% were initially missed by expert WSI screening and added through a deep learning-based pipeline for identification of potential candidates. For algorithmic validation, we used a two-stage approach (RetinaNet followed by cell classificator), which yielded a F1 score of 0.820. Through the algorith- m-aided completion of the dataset we were able to increase the F1 score by 3.4 percentage points. Influence of the size of the dataset was assessed by stepwise reduction of the number of WSI and size (in high power fields, HPF) of the image sections used for training. With the number of included images, the F1 score moderately increased (3 WSI: 0.772; 6 WSI: 0.804; 12 WSI: 0.817; 21 WSI: 0.820). The size of the tumor area in training (ROI selected by an expert) had significant effects on the F1 score (5 HPF: 0.583; 10 HPF: 0.676; 50 HPF: 0.770; complete WSI: 0.820), which was determined in entire WSI of the test set. We emphasize the benefit of appropriate dataset size and complete WSI labeling.
Keywords: mitotic figures; tumor diagnostics; open data; big data; canine cutaneous mast cell tumor

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Taubmann, Oliver; Mühlberg, Alexander; Allmendinger, Thomas; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buss, Sebastian J.; Görich, Johannes; Sühling, Michael; et al. (Scientific Reports, 2023)
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters-including denoising strength, slab combination, and reconstruction kernel-needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.
CRIS-Team Scopus Importer:2023-02-24

How to Get the Most Out of U-Net for Glacier Calving Front Segmentation - Periyasamy, Maniraman; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022)
The melting of ice sheets and glaciers is one of the main contributors to global sea-level rise. Hence, continuous monitoring of glacier changes and in particular the mapping of positional changes of their calving front is of significant importance. This delineation process, in general, has been carried out manually, which is time-consuming and not feasible for the abundance of available data within the past decade. Automatic delineation of the glacier fronts in synthetic aperture radar (SAR) images can be performed using deep learning-based U-Net models. This article aims to study and survey the components of a U-Net model and optimize the model to get the most out of U-Net for glacier (calving front) segmentation. We trained the U-Net to segment the SAR images of Sjogren-Inlet and Dinsmoore-Bombardier-Edgworth glacier systems on the Antarctica Peninsula region taken by ERS-1/2, Envisat, RadarSAT-1, ALOS, TerraSARX, and TanDEM-X missions. The U-Net model was optimized in six aspects. The first two aspects, namely data pre-processing and data augmentation, enhanced the representation of information in the image. The remaining four aspects optimized the feature extraction of U-Net by finding the best-suited loss function, bottleneck, normalization technique, and dropouts for the glacier segmentation task. The optimized U-Net model achieves a dice coefficient score of 0.9378 with a 20% improvement over the baseline U-Net model, which achieved a score of 0.7377. This segmentation result is further post-processed to delineate the calving front. The optimized U-Net model shows 23% improvement in the glacier front delineation compared to the baseline model.
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Keywords: Antarctica; Deep learning; Glacier calving front segmentation; Ice; Image segmentation; Optimized U-Net; Radar polarimetry; Semantic Segmentation; Synthetic aperture radar; Training

How To See Smells: Extracting Olfactory References from Artworks - Zinnen, Mathias (Companion Proceedings of the Web Conference 2021 (WWW '21 Companion), 2021)

How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies - Tayebi Arasteh, Soroosh; Monajem, Mehrpad; Christlein, Vincent; Heinrich, Philipp; Nicolao, Anguelos; Boldaji, Hamidreza Naderi; Lotfinia, Mahshad; Evert, Stephanie (2021 IEEE 15th International Conference on Semantic Computing (ICSC), 2021)
Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETwEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step. Second, we use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets. The resulting classifier is evaluated on the new ReTweeT dataset, showing promising results, especially considering that it has been trained without any manually labeled data. Both the dataset and the baseline implementation are publicly available.<br
Keywords: Deep Learning; Sentiment Analysis; Tweet Reply Prediction; Natural Language Processing

Hybrid OCT-OCTA Vessel Visualization for Projection-Free Display of the Intermediate and Deep Retinal Plexuses - Ploner, Stefan; Moult, Eric M.; Schottenhamml, Julia; Husvogt, Lennart; Lu, Chen D.; Rebhun, Carl B.; Alibhai, A. Yasin; Duker, Jay S.; Waheed, Nadia K.; Maier, Andreas; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : In optical coherence tomography angiography (OCTA), projection artifacts (also shadowing artifacts or decorrelation tails) cause superficial retinal vasculature to appear in the segmented OCTA images of the intermediate and deep retinal plexuses. The projections of these larger superficial vessels obfuscate the unique vascular patterning of the deeper layers. Several algorithms have been proposed to remove these shadows. However, by removing the projected vessels these approaches also decrease the contrast of, or fully eliminate, the underlying vasculature, thereby introducing discontinuities. Thus, in effect, one artifact is replaced with another. The purpose of this study is to develop a projection artifact removal scheme that overcomes this limitation and fully preserves the intermediate/deep retinal vasculature. Methods : Amplitude decorrelation based OCTA data was collected using a 1050nm swept source OCT system. A hybrid OCT-OCTA vessel visualization scheme (described in Figure 1) was developed. The key advantage of our proposed scheme is that the OCT signal is used to adaptively remove the OCTA signal only in the intercapillary regions. Results : The hybrid OCT-OCTA approach reduces projection artifacts to a negligible level in the intermediate and deep plexuses without introducing vessel discontinuity artifacts (Figure 2). Conclusions : Hybrid OCT-OCTA vessel visualization is a promising approach to visualize the unique patterning of the intermediate and deep plexuses, and is likely to be particularly important in diseases such as diabetic retinopathy where it is desirable to separately analyze the retinal plexuse
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.hybrid
Keywords: oct; octa; oct angiography; shadow artifact removal; projection artifact removal

Hybrid Training Data for Historical Text OCR - Martínek, Jiří; Lenc, Ladislav; Král, Pavel; Nikolaou, Angelos; Christlein, Vincent (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art system

Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings - Davari, Amirabbas; Sakaltras, Nikolaos; Haeberle, Armin; Vesal, Sulaiman; Christlein, Vincent; Maier, Andreas; Riess, Christian (International Conference on Image Processing (ICIP), 2018)
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). We show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking is an important preprocessing step towards that goal. Our comparative results with other features underline the efficacy of the three proposed improvements.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.hyperh_9
Keywords: EMAP; Hyper-Hue; Layer Separation; Old Master Drawing; Spectral Focus Stacking

Hypernasality in Speech of Children with Cleft Lip and Palate: Automatic Evaluation - Maier, Andreas; Schuster, Maria; Haderlein, Tino; Nöth, Elmar (Proceedings of the 8th International Seminar on Speech Production 2008, 2008)

Hyperspectral Image Analysis using Limited Data - Davari, Amirabbas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2022)
This thesis studies the use case of hyperspectral images in two applications, namely remote sensing and art history. The common challenge that is present in both these applications is the limited availability of labeled data. This limitation is caused by the tedious, time-consuming, and expensive manual data labeling process by the experts in each respective field. At the same time, hyperspectral images and their feature vectors are typically significantly high dimensional. Combination of these two challenge the supervised machine learning algorithms. In order to tackle this problem, this work proposes to either adapt the limited data to the classifier, or adapt the classifier to the limited training data. Any discrete data can be assumed samples from an unknown distribution which is not accessible. Having access to this underlying distribution enables drawing infinite number of data points. Motivated by this idea, this work takes advantage of Gaussian mixture model (GMM) to estimate the underlying distribution of each class in the dataset. Considering the limited available data, in order to limit the number of parameters, GMMs are constrained to have diagonal covariance matrix. Both on phantom data and the real hyperspectral images, it has been shown that adding only a few synthetic training samples significantly improves the untuned classifier's performance. Further, it has been observed that the untuned classifiers reinforced with the synthesized training samples outperform the tuned classifier's performance on the original training set. The latter suggests that the synthetic samples can replace the expensive parameter tuning process in classifiers. In a different approach, this work proposes to adapt the classifier to the limited data. Traditional classifiers with high capacity often overfit on extremely small training data sets. The Bayesian learning regime has a hardcoded regularization property in its formulation. This property motivates the idea of using Bayesian neural networks to remedy the overfitting problem of the normal (frequentist) convolutional neural networks (CNNs). The experimental results demonstrate that for the same convolutional network architecture, the Bayesian variant outperforms the frequentist version. Using ensemble learning on the sample networks drawn from the Bayesian network further improves the classification performance. Moreover, studying the evolution of the train and validation loss plots for both the Bayesian and the frequentist CNN clearly shows that the Bayesian CNN is significantly more robust against overfitting in the case of extremely limited training data and has higher generalization capability in this situation. For the second application, i.e., the layer separation in the old master drawings, this work studies the effectiveness of hyperspectral images, introduces to use the extended multi-attribute profiles (EMAPs) and hyper-hue features, and compares them against the other state-of-the-art features, using synthesized and real data. The results show that the EMAPs and hyper-hue are more informative and representative feature spaces. Mapping the HS images to these spaces resulted in more accurate color pigment layer segmentation.<br
Keywords: Bayesian Learning; Convolutional Neural Network (CNN); Gaussian Mixture Model (GMM); Hyperspectral Image Classification

ICC++: Explainable Feature Learning for Art History using Image Compositions - Madhu, Prathmesh; Marquart, Tilman; Kosti, Ronak Vijaypal; Suckow, Dirk; Bell, Peter; Maier, Andreas; Christlein, Vincent (Pattern Recognition, 2022)
Image compositions are helpful in the study of image structures and assist in discovering the semantics of the underlying scene portrayed across art forms and styles. With the digitization of artworks in recent years, thousands of images of a particular scene or narrative could potentially be linked together. However, manually linking this data with consistent objectiveness can be a highly challenging and time-consuming task. In this work, we present a novel approach called Image Composition Canvas (ICC) to compare and retrieve images having similar compositional elements. ICC is an improvement over ICC, specializing in generating low and high-level features (compositional elements) motivated by Max Imdahl’s work. To this end, we present a rigorous quantitative and qualitative comparison of our approach with traditional and state-of-the-art (SOTA) methods showing that our proposed method outperforms all of them. In combination with deep features, our method outperforms the best deep learning-based method, opening the research direction for explainable machine learning for digital humanities. We will release the code and the data post-publication.<br

ICDAR 2017 Competition on Historical Document Writer Identification (Historical-WI) - Fiel, Stefan; Kleber, Florian; Diem, Markus; Christlein, Vincent; Louloudis, Georgios; Stamatopoulos, Nikolaos; Gatos, Basilis (14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017)
The ICDAR 2017 Competition on Historical Document Writer Identification is dedicated to record the most recent advances made in the field of writer identification. The goal of the writer identification task is the retrieval of pages,which have been written by the same author. The test dataset used in this competition consists of 3600 handwritten pagesoriginating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer contributed fivepages. This paper describes the dataset, as well as the details ofthe competition. Five different institutions submitted six methods which were ranked using identification and retrieval metrics. The paper describes the competition details including the dataset, the evaluation measures used as well as a short description of each submitted metho
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.icdar2
Keywords: writer identification; competition; writer retrieval

ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents - Christlein, Vincent; Nikolaou, Angelos; Seuret, Mathias; Stutzmann, Dominique; Maier, Andreas (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
This competition investigates the performance of large-scale retrieval of historical document images based on writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval. The most teams submitted traditional methods not using deep learning techniques. The competition results show that a combination of methods is outperforming single methods. Furthermore, letters are much more difficult to retrieve than manuscripts.

ICDAR 2021 Competition on Historical Document Classification - Seuret, Mathias; Nicolaou, Anguelos; Rodríguez-Salas, Dalia; Weichselbaumer, Nikolaus; Stutzmann, Dominique; Mayr, Martin; Maier, Andreas; Christlein, Vincent (Document Analysis and Recognition – ICDAR 2021, 2021)
This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font group recognition while the script type classification and location classification were more challenging. In the dating task, the best system achieved a mean absolute error of about 22 years.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Dating; Document analysis; Document classification; Historical document images

ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri - Seuret, Mathias; Marthot-Santaniello, Isabelle; White, Stephen A.; Serbaeva Saraogi, Olga; Agolli, Selaudin; Carrière, Guillaume; Rodriguez Salas, Dalia; Christlein, Vincent (Document Analysis and Recognition - ICDAR 2023, 2023)
This competition investigates the performance of glyph detection and recognition on a very challenging type of historical document: Greek papyri. The detection and recognition of Greek letters on papyri is a preliminary step for computational analysis of handwriting that can lead to major steps forward in our understanding of this major source of information on Antiquity. It can be done manually by trained papyrologists. It is however a time-consuming task that would need automatising. We provide two different tasks: localization and classification. The document images are provided by several institutions and are representative of the diversity of book hands on papyri (a millennium time span, various script styles, provenance, states of preservation, means of digitization and resolution). <br
Keywords: Document analysis; papyrus; character spotting

ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments - Seuret, Mathias; Nicolaou, Anguelos; Stutzmann, Dominique; Maier, Andreas; Christlein, Vincent (17th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2020)
This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide information on what kind of information each method makes better use of and indirectly contribute to the interpretability of the participating method. Therefore, we created a large dataset consisting of more than 120 000 fragments. Although the most teams submitted methods based on convolutional neural networks, the winning entry achieves an mAP below 40 %.<br
Keywords: Shape; Training; Feature extraction; Measurement; Text analysis; Image retrieval; Image edge detection

Ideal Seed Point Location Approximation for GrowCut Interactive Image Segmentation - Amrehn, Mario; Strumia, Maddalena; Steidl, Stefan; Horz, Tim; Kowarschik, Markus; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
The C-arm CT X-ray acquisition process is a common modality in medical imaging. After image formation, anatomical structures can be extracted via segmentation. Interactive segmentation methods bear the advantage of a dynamically adjustable trade-off between time and achieved segmentation quality for the object of interest w. r. t. fully automated approaches. The segmentation’s quality can be measured in terms of the Dice coefficient with the ground truth segmentation image. A user’s interaction traditionally consist of drawing pictorial hints on an overlay image to the acquired image data via a graphical user interface (UI). The quality of a segmentation utilizing a set of drawn seeds varies depending on the location of the seed points in the image. In this paper, we (1) investigate the influence of seed point location on segmentation quality and (2) propose an approximation framework for ideal seed placements utilizing an extension of the well established GrowCut segmentation algorithm and (3) introduce a user interface for the utilization of the suggested seed point locations. An extensive evaluation of the predictive power of seed importance is conducted from hepatic lesion input images. As a result, our approach suggests seed points with a median of 72.5% of the ideal seed points’ associated Dice scores, which is an increase of 8.4% points to sampling the seed location at random.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.ideals_4

Identification of High-risk Metastases in MRI Follow-up after Stereotaxic by using Radiomics and Delta Radiomics - Sommer, Phillip; Huang, Yixing; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Dörfler, Arnd; Schmidt, M.; Frey, Benjamin; Maier, Andreas; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

IJCARS: BVM 2019 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2019)
CRIS-Team Scopus Importer:2019-11-19

IJCARS: BVM 2021 special issue - Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (International Journal of Computer Assisted Radiology and Surgery, 2021)
CRIS-Team Scopus Importer:2021-11-26

Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT - Müller, Kerstin; Maier, Andreas; Schwemmer, Chris; Lauritsch, Günter; De Buck, Stijn; Wielandts, Jean-Yves; Hornegger, Joachim; Fahrig, Rebecca (Physics in Medicine and Biology, 2014)
The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.imagea_5
Keywords: C-arm CT; cardiac imaging; dynamic imaging; motion compensation; motion-compensated tomographic reconstruction

Image Artifact Influence on Motion Compensated Tomographic Reconstruction in Cardiac C-arm CT - Müller, Kerstin; Schwemmer, Chris; Lauritsch, Günter; Rohkohl, Christopher; Maier, Andreas; Heidbüchel, Hein; De Buck, Stijn; Nuyens, Dieter; Kyriakou, Yiannis; Köhler, Christopher; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the 12th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Image-based Compensation for Involuntary Motion in Weight-bearing C-arm Cone-beam CT Scanning of Knees - Unberath, Mathias; Choi, Jang-Hwan; Berger, Martin; Maier, Andreas; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2015, 2015)
We previously introduced four fiducial marker-based strategies to compensate for involuntary knee-joint motion during weight-bearing C-arm CT scanning of the lower body. 2D methods showed significant reduction of motion-related artifacts, but 3D methods worked best. However, previous methods led to increased examination times and patient discomfort caused by the marker attachment process. Moreover, sub-optimal marker placement may lead to decreased marker detectability and therefore unstable motion estimates. In order to reduce overall patient discomfort, we developed a new image-based 2D projection shifting method. A C-arm cone-beam CT system was used to acquire projection images of five healthy volunteers at various flexion angles. Projection matrices for the horizontal scanning trajectory were calibrated using the Siemens standard PDS-2 phantom. The initial reconstruction was forward projected using maximum-intensity projections (MIP), yielding an estimate of a static scan. This estimate was then used to obtain the 2D projection shifts via registration. For the scan with the most motion, the proposed method reproduced the marker-based results with a mean error of 2.90 mm +/-1.43 mm (compared to a mean error of 4.10 mm +/-3.03 mm in the uncorrected case). Bone contour surrounding modeling clay layer was improved. The proposed method is a first step towards automatic image-based, marker-free motion-compensation.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.imageb
Keywords: GRK-1773

Image-based Detection of MRI Hardware Failures - Jain, Bhavya; Kuhnert, Nadine; deOliveira, Andre; Maier, Andreas (2019)

Image fusion in X-ray differential phase-contrast imaging - Haas, Wilhelm; Polyanskaya, Maria; Bayer, Florian; Gödel, Karl Christoph; Hofmann, Hannes; Rieger, Jens; Ritter, André; Weber, Thomas; Wucherer, Lukas; Durst, Jürgen; Michel, Thilo; Anton, Gisela; et al. (Medical Imaging 2012: Image Processing, 2012)
Phase-contrast imaging is a novel modality in the field of medical X-ray imaging. The pioneer method is the grating-based interferometry which has no special requirements to the X-ray source and object size. Furthermore, it provides three different types of information of an investigated object simultaneously - absorption, differential phase-contrast and dark-field images. Differential phase-contrast and dark-field images represent a completely new information which has not yet been investigated and studied in context of medical imaging. In order to introduce phase-contrast imaging as a new modality into medical environment the resulting information about the object has to be correctly interpreted. The three output images reflect different properties of the same object the main challenge is to combine and visualize these data in such a way that it diminish the information explosion and reduce the complexity of its interpretation. This paper presents an intuitive image fusion approach which allows to operate with grating-based phase-contrast images. It combines information of the three different images and provides a single image. The approach is implemented in a fusion framework which is aimed to support physicians in study and analysis. The framework provides the user with an intuitive graphical user interface allowing to control the fusion process. The example given in this work shows the functionality of the proposed method and the great potential of phase-contrast imaging in medical practice. © 2012 SPIE.

Image Preprocessing Pipeline for Bright-Field Miniature Live Cell Microscopy Prototypes - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.imagep

Image Quality Analysis of Limited Angle Tomography Using the Shift-Variant Data Loss Model - Huang, Yixing; Lauritsch, Günter; Amrehn, Mario; Taubmann, Oliver; Haase, Viktor; Stromer, Daniel; Huang, Xiaolin; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.imageq_5

Image Quality Assessment for Extended-Volume C-arm CT Using a Multi-Turn Reverse Helix - Yu, Zhicong; Maier, Andreas; Lauritsch, Günter; Vogt, Florian; Schönborn, Manfred; Köhler, Christoph; Hornegger, Joachim; Noo, Frédéric (Proc. of IEEE NUCLEAR SCIENCE SYMPOSIUM & MEDICAL IMAGING CONFERENCE, 2014)
UnivIS-Import:2017-12-20:Pub.2014.tech.IMMD.IMMD5.imageq

Image Quality Assessment for Rigid Motion Compensation - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (MedNeurips, 2019)

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI. - Kapsner, Lorenz; Balbach, Eva; Folle, Lukas; Laun, Frederik Bernd; Nagel, Armin Michael; Liebert, Andrzej; Emons, Julius; Ohlmeyer, Sabine; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian (Scientific Reports, 2023)
The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.
EVALuna2:539553

Image Reconstruction from Real Dual Energy CT Data Using an Analytical Energy Response Model - Haase, Viktor; Hahn, Katharina; Schondube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frederic (2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022, 2021)
Dual energy computed tomography (DECT) enables access to the energy-dependence of the linear attenuation coefficient of X-rays. Such information can be used to perform material decomposition; and to subsequently compute mono-energetic images for contrast improvement and reduction of beam hardening artifacts compared to images obtained from single energy computed tomography (CT) systems. Last year at IEEE MIC, we introduced an analytical energy response model with embedded patient-specific scatter correction and demonstrated that this model performs well in the context of single material beam hardening correction. Here, the value of the model is investigated for material decomposition of real dual energy CT data. Specifically, we demonstrate that the model enables robust material decomposition for the ACR phantom from a state-of-the-art diagnostic CT scanner, under the assumption that the two scans are acquired in the same geometry.
CRIS-Team Scopus Importer:2023-03-07

Image Reconstruction with Variational Networks: Application to Synchrotron Radiation Imaging - Rashed, Essam A.; Kudo, Hiroyuki; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018)
In computed tomography, image reconstructed from limited projection data is subject to strong noise and artifacts. Reducing the number of projection views in synchrotron radiation imaging has a great benefits in decreasing the computation cost for image formation. Moreover, it also prevents the damage of biological specimen caused by the x-ray radiation exposure. In this paper, a new procedure for image reconstruction from limited projection views with the assist of deep learning approach is proposed. A deep learning approach is employed to estimate an initial guess of the tomographic image followed by a fast row-action reconstruction to guarantee the restoration of any pattern missed in the first stage. Most of previous attempts that use deep learning in image reconstruction are implemented as a post-processing techniques applied on the reconstructed image. This may lead to anomalies in the final result due to the lack of consistency between the acquired projection data and the reconstructed image. The proposed procedure solves this problem by keeping the data consistency up to the final stage of image formation. This would reduce the possibility of losing image abnormal structures due to insufficient training in deep learning. Experimental results using synchrotron radiation data demonstrate the usefulness of the proposed framework.
CRIS-Team Scopus Importer:2022-06-05

Image Registration for the Alignment of Digitized Historical Documents - Davari, Amirabbas; Lindenberger, Tobias; Haeberle, Armin; Christlein, Vincent; Maier, Andreas; Riess, Christian (2017)

Image retrieval in big image data - Conjeti, Sailesh; Demirci, Stefanie; Christlein, Vincent (Elsevier, 2023)
Image retrieval is a common technique to find similar images in a dataset given a query image. Given such an image, this can for example be used to find images showing similar pathologies and in this way speed up the medical decision process. This chapter gives an introduction into the field of image retrieval. At first,methods to form a global representation given local descriptors in an image are presented. Those methods are then related to deep-learning-based methods. Eventually, basic techniques to speed up the querying using hashing are outlined.
CRIS-Team Scopus Importer:2023-11-10
Keywords: Hashing; Image retrieval; Indexing

Imaging in inflammatory arthritis: progress towards precision medicine - Minopoulou, Ioanna; Kleyer, Arnd; Yalcin-Mutlu, Melek; Fagni, Filippo; Kemenes, Stephan; Schmidkonz, Christian; Atzinger, Armin; Pachowsky, Milena; Engel, Klaus; Folle, Lukas; Roemer, Frank; Waldner, Maximilian; et al. (Nature Reviews Rheumatology, 2023)
Imaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today’s technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer.
CRIS-Team Scopus Importer:2023-09-29

Imitation learning network for fundus image registration using a divide-and-conquer approach - Bayer, Siming; Zhong, Xia; Fu, Weilin; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2020)
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are diffcult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95%.
CRIS-Team Scopus Importer:2020-04-21

Immersive Painting - Soutschek, Stefan; Hönig, Florian Thomas; Maier, Andreas; Steidl, Stefan; Stürmer, Michael; Erzigkeit, Helmut; Hornegger, Joachim; Kornhuber, Johannes (Proceedings of the ArtsIT 2009, 2009)

Impact of acquisition area on deep-learning-based glaucoma detection in OCTA - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Maier, Andreas; Hohberger, Bettina; Mardin, Christian Y. (Investigative Ophthalmology & Visual Science, 2023)

Impact of Including Pathological Speech in Pre-training on Pathology Detection - Weise, Tobias; Maier, Andreas; Demir, Kubilay Can; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Heismann, Björn; Nöth, Elmar; Schuster, Maria Elke; Yang, Seung Hee (Springer Science and Business Media Deutschland GmbH, 2023)
Keywords: transformer; wav2vec2.0; self-supervised learning; pathological speech

Impact of Including Pathological Speech in Pre-training on Pathology Detection - Weise, Tobias; Maier, Andreas; Demir, Kubilay Can; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Heismann, Björn; Nöth, Elmar; Schuster, Maria; Yang, Seung Hee (Text, Speech, and Dialogue, 2023)
Transfer learning has achieved state-of-the-art performance across many different areas, requiring magnitudes less labeled data compared to traditional methods. Pre-trained weights are learned in a self-supervised way on large amounts of unlabeled data, which are fine-tuned for the desired downstream task using labeled data. An example of this in the speech domain is the wav2vec2.0 framework, which was originally designed for automatic speech recognition (ASR) but can also be fine-tuned for general sequence classification tasks. This paper analyses the effects of including pathological speech during the pre-training of wav2vec2.0, where quantized speech representations are learned, on the performance of a fine-tuned pathology detection task. We show that this architecture can be successfully fine-tuned for cleft lip and palate (CLP) detection, where the best-performing model yields an F1-score of when pre-trained on healthy speech only. Our experiments show, that including pathological speech during pre-training drastically degrades the performance on detection of the same pathology for which it was fine-tuned. The worst-performing model was pre-trained exclusively on CLP speech, resulting in an F1-score of . Whilst performed experiments only focus on CLP, the magnitude of the results suggest, that other pathologies will also follow this tren
Keywords: wav2vec2.0; self-supervised learning; transformer; pathological speech

Impact of Including Pathological Speech in Pre-Training on Pathology Detection - Yang, Seung Hee; Weise, Tobias; Demir, Kubilay Can (Text, Speech, and Dialogue. Satellite event of Interspeech 22023, 2023)
Transfer learning has achieved state-of-the-art performance across many different areas, requiring magnitudes less labeled data compared to traditional methods. Pre-trained weights are learned in a self-supervised way on large amounts of unlabeled data, which are fine-tuned for the desired downstream task using labeled data. An example of this in the speech domain is the wav2vec2.0 framework, which was originally designed for automatic speech recognition (ASR) but can also be fine-tuned for general sequence classification tasks. This paper analyses the effects of including pathological speech during the pretraining of wav2vec2.0, where quantized speech representations are learned, on the performance of a fine-tuned pathology detection task. We show that this architecture can be successfully fine-tuned for cleft lip and palate (CLP) detection, where the best-performing model yields an F1-score of 82.3% when pre-trained on healthy speech only. Our experiments show, that including pathological speech during pre-training drastically degrades the performance on detection of the same pathology for which it was fine-tuned. The worst-performing model was pretrained exclusively on CLP speech, resulting in an F1-score of 33.9%. Whilst performed experiments only focus on CLP, the magnitude of the results suggest, that other pathologies will also follow this trend.<br
Keywords: wav2vec2.0; self-supervised learning; transformer; pathological speech

Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma - Sievert, Matti; Eckstein, Markus; Mantsopoulos, Konstantinos; Mueller, Sarina K.; Stelzle, Florian; Aubreville, Marc; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; Goncalves, Miguel (European Archives of Oto-Rhino-Laryngology, 2021)
Purpose Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE.
CRIS-Team WoS Importer:2021-07-23

Impact of Pathologies on Automatic Age Estimation - Schwinn, Leo; Haderlein, Tino; Nöth, Elmar; Maier, Andreas (Fortschritte der Akustik - DAGA 2019, 2019)
Automatic Age estimation using speech is a challenging problem. Among other things, the many influences that lead to a change in the voice make it difficult to estimate the exact age. Examples are the microphone used, the distance to this microphone or the sex of the speaker. Stable results can be achieved by extracting Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal and processing them into i‑vectors, classification and regression tools such as Support Vector Regression (SVR) can then be used for the age estimation from these features. An additional factor influencing the age estimation are speech or voice disorders of a speaker. Rarely is this impact assessed, resulting in systems that are not tailored to the needs of pathological speakers like Siri or Amazon echo. Another example are companies which use automatic age estimation to forward calls to persons of the same age as the caller. These systems are also adapted to the characteristics of healthy speakers. This paper examines the impacts of such pathologies on age estimation and assess the possibility of reducing their influence by using the Word Accuracy (WA) of the speakers. This measure gives information about the intelligibility of a speaker and should help to reduce the variance of the extracted features. The features with low variance should provide more stable results in the age estimation. To achieve this, we compare the results of an age estimation for four different groups of speakers. All speakers at once, only pathological speakers, only healthy speakers and training the SVR with healthy speakers while testing with pathological speakers. Each of these groups are further separated by the WA of the speaker
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.impact
Keywords: age estimation; speech recognition; speaker characteristics

Impact of the non-negativity constraint in model-based iterative reconstruction from CT data - Haase, Viktor; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (Medical Physics, 2019)
Model-based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x-ray computed tomography (CT). In the problem formulation, it is common to enforce non-negative values to accommodate the physical non-negativity of x-ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non-negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non-negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint. We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non-negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non-negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth. For cases with moderate inconsistencies in the data, associated with noise and bone-induced beam hardening, our results show that the non-negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge-preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint. Our results did not highlight the non-negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non-negativity constraint could be disregarded to simplify the optimization problem or to adopt other forward projection models that require complex optimization machinery to be used together with non-negativity.
Keywords: model-based iterative reconstruction; non-negativity constraint; penalized weighted least-squares; positivity constraint; x-ray computed tomography

Implant-based CT estimation towards adaptive breast brachytherapy - Dürrbeck, Christopher; Pflaum, L.; Schulz, M.; Kallis, Karoline; Geimer, Tobias; Abu-Hossin, N.; Strnad, Vratislav; Maier, Andreas; Fietkau, Rainer; Bert, Christoph (RADIOTHERAPY AND ONCOLOGY, 2021)
CRIS-Team WoS Importer:2021-06-18

Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses - Ellmann, Stephan; Wenkel, Evelyn; Dietzel, Matthias; Bielowski, Christian; Vesal, Sulaiman; Maier, Andreas; Hammon, Matthias; Janka, Rolf Matthias; Fasching, Peter; Beckmann, Matthias; Schulz-Wendtland, Rüdiger; Uder, Michael; et al. (PLoS ONE, 2020)
We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81–0.98) with variable diagnostic accuracy (AUC: 0.65–0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies.<br

Implementation of the FDK algorithm for cone-beam CT on the cell broadband engine architecture - Scherl, Holger; Körner, Mario; Hofmann, Hannes; Eckert, Wieland; Kowarschik, Markus; Hornegger, Joachim (Medical Imaging 2007: Physics of Medical Imaging, 2007)
In most of today's commercially available cone-beam CT scanners, the well known FDK method is used for solving the 3D reconstruction task. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. The brand-new Cell Broadband Engine Architecture (CBEA) with its high level of parallelism is a cost-efficient processor for performing the FDK reconstruction according to the medical requirements. The programming scheme, however, is quite different to any standard personal computer hardware. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CBEA. Our software framework allows to compute the filtering and back-projection in parallel, making it possible to do an on-the-fly-reconstruction. The achieved results demonstrate that a complete FDK reconstruction is computed with the CBEA in less than seven seconds for a standard clinical scenario. Given the fact that scan times are usually much higher, we conclude that reconstruction is finished right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device.

Implications of Experiment Set-Ups for Residential Water End-Use Classification - Gourmelon, Nora; Bayer, Siming; Mayle, Michael; Bach, Guy; Bebber, Christian; Munck, Christophe; Sosna, Christoph; Maier, Andreas (5th International Electronic Conference on Water Sciences, 2020)
With the increased population in urban areas worldwide, the security of water supply is gaining in importance. Water scarcity accelerated by climate change poses additional stress to water supply infrastructures. Water consumption data transmitted by smart water meters form the foundation of advanced data analysis, such as water end-use classification, with which the resilience of water supply can be improved. Especially with large amounts of high-resolution data, the accurate categorization of data from smart water meters into different end-uses such as toilets, showers or dishwashers is challenging and cannot be performed by humans. To this end, machine-learning (ML) approaches provide several benefits, such as real-time capability, scalability and generalizability. State-of-the-art methods to identify residential water end-uses include both unsupervised methods and supervised approaches. However, a comprehensive comparison of unsupervised and supervised techniques is still missing. In this study, we are aiming at a quantitative evaluation of various ML techniques for water end-use classification. Furthermore, we focus on deriving general implications on the setting and conduction of ML-based experiments for water end-use classification. For these purposes, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. The major implications drawn from the quantitative results are two-fold: clustering is not suitable to separate end-use categories. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpose
Keywords: end-use classification; smart water meter; machine learning

Implications of Experiment Set-Ups for Residential Water End-Use Classification - Gourmelon, Nora; Bayer, Siming; Mayle, Michael; Bach, Guy; Bebber, Christian; Munck, Christophe; Sosna, Christoph; Maier, Andreas (Water, 2021)
With an increasing need for secured water supply, a better understanding of the water consumption behavior is beneficial. This can be achieved through end-use classification, i.e., identifying end-uses such as toilets, showers or dishwashers from water consumption data. Previously, both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets. However, a comprehensive comparison of ML techniques on a common dataset is still missing. Hence, in this study, we are aiming at a quantitative evaluation of various ML techniques on a common dataset. For this purpose, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. In conclusion, clustering techniques alone are not suitable to separate end-use categories fully automatically. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpos
Keywords: end-use classification; smart water meter; machine learning; residential water

Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA - Sohani, Majid; van der Geest, Rob J.; Maier, Andreas; Powell, Andrew J.; Moghari, Mehdi H. (Magnetic Resonance Imaging, 2022)
Purpose: To develop and validate a new myocardial T1 mapping sequence (MOSHA) which is based on a combination of the modified Look-Locker inversion recovery (MOLLI) and the saturation recovery single-shot acquisition (SASHA) sequences. Methods: Prior studies have shown that myocardial T1 mapping by SASHA is more accurate but less precise than MOLLI. A new myocardial T1 mapping technique (MOSHA) based on single-shot acquisitions is developed by combining the MOLLI and SASHA sequences. Phantom and patient studies on 15 patients (9 males, median age 21 years) were performed to validate and compare MOSHA with the MOLLI and SASHA sequences in terms of accuracy and precision. Results: In the phantom study, MOSHA was as accurate as SASHA (P-value = 0.88) and as precise as MOLLI (P-value = 0.59). Similar trends were observed in the patient study. Compared to SASHA, MOSHA accuracy was comparable for blood pre-contrast (P-value≥0.10) and post-contrast (P-value≥0.70), and for myocardium pre-contrast (P-value = 0.70) and post-contrast (P-value = 0.09). Compared to MOLLI, MOSHA precision was lower for blood pre-contrast (P-value<0.01) and higher for blood post-contrast (P-value≤0.01), and comparable for myocardium pre-contrast (P-value = 0.24) and post-contrast (P-value = 0.07). Synthetic Extracellular volume fraction (ECV) calculated by MOSHA was more precise than those of SASHA and MOLLI (P-value≤0.01). Conclusion: In phantom studies and patients, MOSHA has comparable accuracy as SASHA and nearly similar precision as MOLLI for T1 mapping. Precision of MOSHA was better than MOLLI and SASHA in synthetic ECV measurements. Therefore, it may be a superior choice in clinical practice for a precise and accurate calculation of T1 and ECV.
CRIS-Team Scopus Importer:2022-03-18
Keywords: Cardiac magnetic resonance; Extracellular volume fraction; Myocardial fibrosis; T; Tissue characterization

Improved HER2 Tumor Segmentation with Subtype Balancing Using Deep Generative Networks - Öttl, Mathias; Steenpaß, Jana; Rübner, Matthias; Geppert, Carol-Immanuel; Qiu, Jingna; Wilm, Frauke; Hartmann, Arndt; Beckmann, Matthias; Fasching, Peter; Maier, Andreas; Erber, Ramona; Breininger, Katharina (Proceedings - International Symposium on Biomedical Imaging, 2023)
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-conditioned deep generative networks and to combine subtype-balanced synthetic images with the original dataset to achieve better segmentation performance. We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology. Additionally, we show the capability of diffusion models to conditionally inpaint HER2 tumor areas with modified subtypes. Combining the original dataset with the same amount of diffusion-generated images increased the tumor Dice score from 0.833 to 0.854 and almost halved the variance between the HER2 subtype recalls. These results create the basis for more reliable automatic HER2 analysis with lower performance variance between individual HER2 subtypes.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Diffusion Models; Generative Models; HER2; Histopathology; Segmentation; Subtypes

Improved OCT motion correction in pathology using advanced eye motion modeling - Ploner, Stefan; Schottenhamml, Julia; Moult, Eric; Husvogt, Lennart; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2021)
CRIS-Team WoS Importer:2021-09-17

Improved Semi-Automatic Basket Catheter Reconstruction from two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.improv_0

Improved trajectories in C-Arm computed tomography for non-circular fields of view - Herbst, Magdalena; Schebesch, Frank; Berger, Martin; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.improv_5
Keywords: GRK-1773

Improved X-Ray Bone Segmentation by Normalization and Augmentation Strategies - Kordon, Florian Johannes; Lasowski, Ruxandra; Swartman, Benedict; Franke, Jochen; Fischer, Peter; Kunze, Holger (Bildverarbeitung für die Medizin 2019, 2019)
X-ray images can show great variation in contrast and noise levels. In addition, important subject structures might be superimposed with surgical tools and implants. As medical image datasets tend to be of small size, these image characteristics are often under-represented. For the task of automated, learning-based segmentation of bone structures, this may lead to poor generalization towards unseen images and consequently limits practical application. In this work, we employ various data augmentation techniques that address X-ray-specific image characteristics and evaluate them on lateral projections of the femur bone. We combine those with data and feature normalization strategies that could prove beneficial to this domain. We show that instance normalization is a viable alternative to batch normalization and demonstrate that contrast scaling and the overlay of surgical tools and implants in the image domain can boost the representational capacity of available image data. By employing our best strategy, we can improve the average symmetric surface distance measure by 36.22
Keywords: X-Ray; Femur; Bone segmentation; Orthopedics

Improvement of a Speech Recognizer for Standardized Medical Assessment of Children's Speech by Integration of Prior Knowledge - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich (Proc. 2nd IEEE Workshop on Spoken Language Technologies (SLT 2010), 2010)

Improvement of Deep Learning-based Auto-detection of Brain Metastases by Integrating previous MRI Images - Dörfler, Arnd; Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Maier, Andreas; Fietkau, Rainer; et al. (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

Improve Path Seeking Accuracy for Iterative Reconstruction Using the Karush-Kuhn-Tucker Conditions - Wu, Meng; Maier, Andreas; Yang, Qiao; Fahrig, Rebecca (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.improv

Improving Generalization Capability of Multiorgan Segmentation Models Using Dual-Energy CT - Chen, Shuqing; Zhong, Xia; Dorn, Sabrina; Ravikumar, Nishant; Tao, Qinghua; Huang, Xiaolin; Lell, Michael; Kachelriess, Marc; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2022)
Multiorgan segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft-tissue contrast, noise, and artifacts. Devising automatic multiorgan segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multicenter/scanner analyses. In this study, we investigate the use of dual-energy CT (DECT) images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e., at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in the frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using DECT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with p ≤ 0.03. Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available DECT data.
CRIS-Team Scopus Importer:2022-01-14
Keywords: Data augmentation; deep learning; dual-energy computed tomography (DECT); multiorgan segmentation

Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies - Nau, Merlin; Vija, Alexander Hans; Reymann, Maximilian; Gohn, Wesley; Maier, Andreas (German Conference on Medical Image Computing - Bildverarbeitung für die Medizin, 2024)
Quantum computing and quantum annealing present promising avenues for addressing complex problems in various fields, including tomographic image reconstruction. This study investigates the application of hybrid quantum annealing in the context of tomographic image reconstruction, focusing on the formulation of compatible conventional image regularization strategies: L2 and total variation. Using a Shepp-Logan phantom of image size 32× 32 with 4-bit grayscale encoding, we study the effect of the regularization techniques under the influence of their parameters and the runtime of the hybrid quantum annealer. The study reveals, that L2 regularization effectively enhances the obtained image reconstructions and total variation can further improve them. Despite efforts to employ regularized hybrid quantum annealing reconstructions, they still fall short in comparison to traditional reconstruction techniques.<br

Improving Parameter Mapping in MRI Relaxometry and Multi-Echo Dixon Using an Automated Spectral Denoising - Lugauer, Felix; Nickel, Dominik; Kannengiesser, Stephan A. R.; Barnes, Samuel; Holshouser, Barbara; Wetzl, Jens; Hornegger, Joachim; Maier, Andreas (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.improv_5

Improving Segmentation Quality in Rotational Angiography Using Epipolar Consistency - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Proceedings of MICCAI CVII-STENT, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.improv_1

Improving the Noise Propagation Behavior of Different Fatty Acid Quantification Techniques using Spectral Denoising - Schneider, Manuel; Lugauer, Felix; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.improv_0

Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Proceedings of the International Society for Magnetic Resonance in Medicine, 2022)

Incremental Dimensionality Reduction for Respiratory Signal Extraction From X-Ray Sequences - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (IGIC 2014 - Abstractband, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.increm_6

Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis - Huang, Xiaolin; Maier, Andreas; Hornegger, Joachim; Suykens, Johan A. K. (Applied and Computational Harmonic Analysis, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.indefi
Keywords: indefinite kernel; LS-SVM; kPCA

Indoor Synthetic Data Generation: A Systematic Review - Schieber, Hannah; Demir, Kubilay Can; Kleinbeck, Constantin; Yang, Seung Hee; Roth, Daniel (Computer Vision and Image Understanding, 2024)
Objective: Deep learning-based object recognition, 6D pose estimation, and semantic scene understanding require a large amount of training data to achieve generalization. Time-consuming annotation processes, privacy, and security aspects lead to a scarcity of real-world datasets. To overcome this lack of data, synthetic data generation has been proposed, including multiple facets in the area of domain randomization to extend the data distribution. The objective of this review is to identify methods applied for synthetic data generation aiming to improve 6D pose estimation, object recognition, and semantic scene understanding in indoor scenarios. We further review methods used to extend the data distribution and discuss best practices to bridge the gap between synthetic and real-world data. Methods: We adhered to the guidelines of the systematic PRISMA technique. Three databases, IEEE Xplore, Springer Link, and ACM, and an additional manual search were conducted. In total, we identified 241 studies and included 34 in our systematic review. Conclusion: In summary, synthetic data generation has been performed using crop-out methods, graphic APIs, 3D modeling or authoring tools, or game engine-based methods. To extend the data distribution, varying scene parameters, i.e., lighting conditions or textures and the use of distracting objects in the scene are promising.
CRIS-Team Scopus Importer:2024-01-19
Keywords: Domain randomization; Indoor synthetic data; Synthetic data generation

Inertial Measurements for Motion Compensation in Weight-Bearing Cone-Beam CT of the Knee - Maier, Jennifer; Nitschke, Marlies; Choi, Jang-Hwan; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Involuntary motion during weight-bearing cone-beam computed tomography (CT) scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagnosis. Currently, image-based or marker-based methods are applied to correct for this motion, but often require long execution or preparation times. We propose to attach an inertial measurement unit (IMU) containing an accelerometer and a gyroscope to the leg of the subject in order to measure the motion during the scan and correct for it. To validate this approach, we present a simulation study using real motion measured with an optical 3D tracking system. With this motion, an XCAT numerical knee phantom is non-rigidly deformed during a simulated CT scan creating motion corrupted projections. A biomechanical model is animated with the same tracked motion in order to generate measurements of an IMU placed below the knee. In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction. Our proposed approach can effectively reduce motion artifacts in the reconstructed volumes. Compared to the motion corrupted case, the average structural similarity index and root mean squared error with respect to the no-motion case improved by 13–21% and 68–70%, respectively. These results are qualitatively and quantitatively on par with a state-of-the-art marker-based method we compared our approach to. The presented study shows the feasibility of this novel approach, and yields promising results towards a purely IMU-based motion compensation in C-arm CT.<br
Keywords: Motion compensation; Inertial measurements; CT reconstruction

Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee - Maier, Jennifer; Nitschke, Marlies; Choi, Jang Hwan; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Inference of the Selective Auditory Attention using Sequential LMMSE Estimation - Kuruvila, Ivine; Demir, Kubilay Can; Fischer, Eghart; Hoppe, Ulrich (IEEE transactions on bio-medical engineering, 2021)
Attentive listening in a multispeaker environment such as a cocktail party requires suppression of the interfering speakers and the noise around. People with normal hearing perform remarkably well in such situations. Analysis of the cortical signals using electroencephalography (EEG) has revealed that the EEG signals track the envelope of the attended speech stronger than that of the interfering speech. This has enabled the development of algorithms that can decode the selective attention of a listener in controlled experimental settings. However, often these algorithms require longer trial duration and computationally expensive calibration to obtain a reliable inference of attention. In this paper, we present a novel framework to decode the attention of a listener within trial durations of the order of two seconds. It comprises of three modules: 1) Dynamic estimation of the temporal response functions (TRF) in every trial using a sequential linear minimum mean squared error (LMMSE) estimator, 2) Extract the N1-P2 peak of the estimated TRF that serves as a marker related to the attentional state and 3) Obtain a probabilistic measure of the attentional state using a support vector machine followed by a logistic regression. The efficacy of the proposed decoding framework was evaluated using EEG data collected from 27 subjects. The total number of electrodes required to infer the attention was four: One for the signal estimation, one for the noise estimation and the other two being the reference and the ground electrodes. Our results make further progress towards the realization of neuro-steered hearing aids.
CRIS-Team Scopus Importer:2021-05-07
Keywords: Auditory system; Brain modeling; Decoding; Electrodes; Electroencephalography; Estimation; Signal resolution

Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment - Wilm, Frauke; Bertram, Christof A.; Marzahl, Christian; Bartel, Alexander; Donovan, Taryn A.; Assenmacher, Charles Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; Dettwiler, Martina; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Influence of Reading Errors on the Text-Based Automatic Evaluation of Pathologic Voices - Haderlein, Tino; Nöth, Elmar; Maier, Andreas; Schuster, Maria; Rosanoski, Frank (Proceedings Text, Speech and Dialogue; 11th International Conference, 2008)

Influence of the phase effect on gradient-based and statistics-based focus measures in bright field microscopy - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas; Buchholz, Rainer; Hornegger, Joachim (Journal of Microscopy, 2014)
Autofocusing is essential to high throughput microscopy and live cell imaging and requires reliable focus measures. Phase objects such as separated single Chinese hamster ovary cells are almost invisible at the optical focus position in bright field microscopy images. Because of the phase effect, defocused images of phase objects have more contrast. In this paper, we show that widely used focus measures exhibit an untypical behaviour for such images. In the case of homogeneous cells, that is, when most cells tend to lie in the same focal plane, both gradient-based and statistics-based focus measures tend to have a local minimum instead of a global maximum at the optical focus position. On the other hand, if images show inhomogeneous cells, gradient-based focus measures tend to yield typical focus curves, whereas statistics-based focus measures deliver curves similar to the case of homogeneous cells. These results were interpreted using the equation describing the phase effect and patch-wise analysis of the focus curves. Bioprocess engineering experts are also influenced by the phase effect. Forty-four focus positions selected by them led to the conclusion that they prefer to look at defocused images instead of those at the optical focus. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.influe
Keywords: Bright field microscopy; Focus measures; Nontypical focus curves; Phase effect

Influence of X-Ray Radiation on Historical Paper - Zippert, Patrick; Seuret, Mathias; Maier, Andreas; Hausotte, Tino (11th Conference on Industrial Computed Tomography, 2022)
Historical books are often so fragile that they cannot easily be opened in order to read the contents without damaging them. Current research shows, that industrial X-ray computed tomography (CT) can be used to examine closed historical manuscripts. However, the effect of X-rays on ancient paper has rarely been studied, making it difficult to assess the impact of CT measurements on fragile historical manuscripts in terms of additional destruction. To address this problem, various types of paper were exposed to high levels of X-ray radiation in order to examine them for change in optical properties. The investigations showed increasing yellowing due to the increased radiation energy. However, the severity of the yellowing is strongly dependent on the composition of the paper.<br
Keywords: Historical documents; CT measurement side effects; radiation damage