A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction - Hammernik, Kerstin; Würfl, Tobias; Pock, Thomas; Maier, Andreas (Proc. Bildverarbeitung für die Medizin (BVM) 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.adeepl

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction - Seitzer, Maximilian; Yang, Guang; Schlemper, Jo; Oktay, Ozan; Würfl, Tobias; Christlein, Vincent; Wong, Tom; Mohiaddin, Raad; Firmin, David; Keegan, Jennifer; Rueckert, Daniel; Maier, Andreas (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I, 2018)
CRIS-Team WoS Importer:2019-08-13

A new calibration-free beam hardening reduction method for industrial CT - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the 8th Conference on Industrial Computed Tomography (iCT) 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.anewca_7

Applications of Data Consistency Conditions in Cone-Beam Computed Tomography - Würfl, Tobias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
The mathematical model of computed tomography data acquisition shows that every such measurement contains a degree of redundancy. This redundancy can be used to formulate data consistency conditions which enable quantifying how accurately a measurement fulfills the mathematical model. This enables compensating for inaccuracies in the measurement process by formulating artifact compensation as optimization problems of an objective function based on such data consistency conditions. In the course of this thesis, various methods to deal with different types of such inaccuracies in the measurement have been proposed. One type of inaccuracy arises due to the simplified physical model of computed tomography reconstruction which assumes a mono-chromatic X-ray source. This leads to an effect commonly named beam hardening. To compensate artifacts from this beam hardening effect a projection-based algorithm named the Empirical Cupping Correction using the Epipolar Consistency Condition (ECC 2 ) algorithm is introduced. It uses a recently proposed data consistency condition called the Grangeat data consistency condition to formulate an efficient procedure to estimate parameters of an appropriate compensation model. This compensation model is derived to be physically plausible by mathematical analysis of the beam hardening effect. This method is extended in a second algorithm named the Multi-material ECC 2 (MECC 2 ) algorithm to incorporate the possibility to compensate artifacts caused by the different energy dependencies of the linear attenuation coefficient of different materials. Efficient parameter estimation techniques and methods for physically plausible constraints on the compensation function are also presented. Another category of inaccuracies in the measurement are caused by inaccurate knowledge of the acquisition geometry or rigid movement of the imaged object. A general strategy to solve both problems is to estimate the geometry of the acquisition projection-based. However, this is difficult for X-ray imaging modalities because standard techniques from computer vision can not be translated straightforward to this setting. This is caused by the fact, that computer vision algorithms rely on identifying corresponding points in multiple pictures of the same scene as a fundamental step. This is very hard in X-ray imaging due to the different image formation model. To solve this issue a method to estimate the relative geometry of two views is introduced and methods to estimate a projective reconstruction based on multiple pairwise geometry estimates.<br

Calibration-free beam hardening reduction in x-ray CBCT using the epipolar consistency condition and physical constraints. - Würfl, Tobias; Hoffmann, Mathis; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Medical Physics, 2019)
The beam hardening effect is a typical source of artifacts in x-ray cone beam computed tomography (CBCT). It causes streaks in reconstructions and corrupted Hounsfield units toward the center of objects, widely known as cupping artifacts. We present a novel efficient projection data-based method for reduction of beam-hardening artifacts and incorporate physical constraints on the shape of the compensation functions. The method is calibration-free and requires no additional knowledge of the scanning setup. The mathematical model of the beam hardening effect caused by a single material is analyzed. We show that the effect of beam hardening on the resulting functions on the line integral measurements are monotonous and concave functions of the ideal data. This holds irrespective of any limiting assumptions on the energy dependency of the material, the detector response or properties of the x-ray source. A regression model for the beam hardening effect respecting these theoretical restrictions is proposed. Subsequently, we present an efficient method to estimate the parameters of this model directly in projection domain using an epipolar consistency condition. Computational efficiency is achieved by exploiting the linearity of an intermediate function in the formulation of our optimization problem. algorithm is effective even in challenging measured data scenarios with additional sources of inconsistency. The combination of mathematical consistency condition and a compensation model that is based on the properties of x-ray physics enables us to improve image quality of measured data retrospectively and to decrease the need for calibration in a data-driven manner. BACKGROUND PURPOSE METHOD RESULTS CONCLUSIONS

Deep Image Denoising in SPECT - Reymann, Maximilian; Würfl, Tobias; Ritt, Philipp; Cachovan, Michal; Stimpel, Bernhard; Maier, Andreas (European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, 2019)
Aim/Introduction: Single Photon Emitted Computed Tomography (SPECT) images are characterized by a high degree of image noise, which is due to the low photon count at the detector. Recently, several deep learning-based approaches for image denoising have been proposed for low-dose Computed Tomography (CT). We investigate their suitability for SPECT denoising. Materials and Methods: In order to have a groundtruth for training the networks, a Monte Carlo Simulation was set up with the SIMIND software, where in total 24 SPECT acquisitions of brains, lungs, livers and skeleton were simulated with the XCAT phantom. Pairs of noisy and clean SPECT images were then used for training the neural networks. The networks tested were the U-Net as proposed by Heinrich et al. [1], the convolutional denoising autoencoder (CNN DAE) proposed by Gondara et al. [2] and a custom U-Net that had a depth of 4 layers with 2 convolutions, Batch Normalization and ReLU in each layer. Training of the networks was done with the hyperparameters as described in the original publications. Results: The best denoising performance of the neural networks was achieved by either the CNN DAE when measured with the Structural Similarity (SSIM) index of 0.933 or by the custom U-Net with a Peak Signal to Noise Ratio (PSNR) of 30.03 dB. This corresponded to a signal quality improvement of 0.09 compared to the noisy input when measured with the SSIM, or 5.9 dB when measured with the PSNR. The denoised SPECT images showed improved visual appearance, however, gain in image quality was not as distinct as on low-dose CT data. We assume this was due to the image characteristics of SPECT images, which are characterized by few distinct image values and a more heterogeneous pixel intensity distribution compared to CT data. Conclusion: Image characteristics in SPECT pose a special challenge to deep learning-based image denoising methods, as the signal is sparse and contains a high degree of noise. We believe this, in conjunction with the limited number of samples explains the weaker benefit in performance in SPECT when compared to low-dose CT data. We assume that with a more diverse training set and specialized loss function suitable for SPECT data , we can further increase performanc
Keywords: Deep Learning; SPECT; Denoising; Monte Carlo Simulation; XCAT; Phantom

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements - Hoffmann, Mathis; Buerhop-Lutz, Claudia; Reeb, Luca; Pickel, Tobias; Winkler, Thilo; Doll, Bernd; Würfl, Tobias; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (Progress in Photovoltaics: Research and Applications, 2021)
Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurementsof modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here,we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error of 9.0+/-3.7W (4.0+/-8.4%). Then, we show that deep-learning can be used to train a model that performs significantly better (7.3+/-2.7W or 3.2+/-6.5%). With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.

Deep Learning Computed Tomography - Würfl, Tobias; Ghesu, Florin-Cristian; Christlein, Vincent; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention MICCAI 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.deeple

Deep Learning Computed Tomography: Learning Projection-Domain Weights from Image Domain in Limited Angle Problems - Würfl, Tobias; Hoffmann, Mathis; Christlein, Vincent; Breininger, Katharina; Huang, Yixing; Unberath, Mathias; Maier, Andreas (IEEE Transactions on Medical Imaging, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.deeple
Keywords: Image reconstruction; Neural networks; Machine learning; Geometry; Reconstruction algorithms; Iterative methods; Computed tomography

Deep Learning for Magnetic Resonance Fingerprinting: Accelerating the Reconstruction of Quantitative Relaxation Maps - Hoppe, Elisabeth; Körzdorfer, Gregor; Nitka, Mathias; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Schneider, Manuel; Pfeuffer, Josef; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.deeple_0
Keywords: Quantitative Magnetic Resonance Imaging; Neural Networks; Convolutional Neural Networks

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series - Hoppe, Elisabeth; Körzdorfer, Gregor; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Pfeuffer, Josef; Maier, Andreas (German Medical Data Sciences: Visions and Bridges, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.deeple_2
Keywords: Deep Learning; Magnetic Resonance Imaging

Deep learning fundamentals - Breininger, Katharina; Christlein, Vincent; Würfl, Tobias; Maier, Andreas (Informatik aktuell, 2018)
Deep learning has received a lot of attention in the machine learning community. Successful applications from speech recognition or computer vision are already part of our daily life. Much effort has been devoted to transferring this success to medical image computing.
CRIS-Team Scopus Importer:2020-11-20

Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (40th German Conference on Pattern Recognition, GCPR 2018, 2019)
Keywords: Hybrid MRI/X-ray imaging; Precision learning; Machine learning

Empirical Scatter Correction using the Epipolar Consistency Condition - Hoffmann, Mathis; Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
Scatter affects every computed tomography (CT) image. Calibration-free software scatter reduction methods have not been used extensively in practice. Recently, consistency conditions have been applied successfully to other artifact reduction problems in CT imaging. We propose a scatter reduction method, that uses an epipolar consistency condition (ECC) to estimate parameters of an additive scatter model. We evaluate our approach by comparing it with an image-based empirical scatter correction method (ESC) that uses the same scatter model. We show that it performs equally well onsimulated data. Further, ECC outperforms ESC regarding the computational load for the determination of the parameters, because ECC is formulated in projection domain such that no image reconstruction is necessary. While some restrictions might apply for the stability of ECC on measured data, no prior information needs to be formulated regarding the reconstructed image, like it is required with ES
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.empiri

Epipolar Consistency Guided Beam Hardening Reduction - ECC² - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Huang, Xiaolin; 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.epipol_8

Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging - Würfl, Tobias; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the IEEE International Conference on Computer Vision, 2019)
We present a general method to estimate the fundamental matrix from a pair of images under perspective projection without the need for image point correspondences. Our method is particularly well-suited for transmission imaging, where state-of-the-art feature detection and matching approaches generally do not perform well. Estimation of the fundamental matrix plays a central role in auto-calibration methods for reflection imaging. Such methods are currently not applicable to transmission imaging. Furthermore, our method extends an existing technique proposed for reflection imaging which potentially avoids the outlier-prone feature matching step from an orthographic projection model to a perspective model. Our method exploits the idea that under a linear attenuation model line integrals along corresponding epipolar lines are equal if we compute their derivatives in orthogonal direction to their common epipolar plane. We use the fundamental matrix to parametrize this equality. Our method estimates the matrix by formulating a non-convex optimization problem, minimizing an error in our measurement of this equality. We believe this technique will enable the application of the large body of work on image-based camera pose estimation to transmission imaging leading to more accurate and more general motion compensation and auto-calibration algorithms, particularly in medical X-ray and Computed Tomography imaging
Keywords: Structure from Motion; CT reconstruction

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

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

Glaucoma detection using non-perfused areas in OCTA - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Lämmer, Robert; Hohberger, Bettina; Maier, Andreas; Mardin, Christian Y. (Scientific Reports, 2024)
Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.
CRIS-Team Scopus Importer:2024-05-10

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Lämmer, Robert; Hohberger, Bettina; Maier, Andreas; Mardin, Christian Y. (Scientific Reports, 2024)
Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valuable information about the retinal microcirculation that has shown to be correlated with the onset of the disease. The vessel density (VD) is the most commonly used biomarker to quantify this vascular information. However, different studies showed that there is a great impact of the acquisition area on the performance of the VD to distinguish between glaucoma patients and a healthy control group. It also seems that the separate capillary plexuses are differently affected by the disease and therefore also influence the results. So in this study we investigate the impact of the acquisition area (3 × 3 mm macular scan, 6.44 × 6.4 mm macular scan, 6 × 6 mm optic nerve head (ONH) scan) and the different plexuses on the machine-learning-based distinction between glaucoma patients and healthy controls. The results yielded that the 6 × 6 mm ONH show the best performance over all plexuses. Moreover the deep learning-based approach outperforms the VD as a biomarker on every acquisition area and plexus. In addition to that, it also performs better than traditional biomarkers obtained from the OCT scans that are used in the clinical routine for diagnosis and progression tracking of glaucoma. Consequently, OCTA scans of the ONH might be a useful addition to OCT when studying glaucoma.
CRIS-Team Scopus Importer:2024-09-13

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)

Learning with Known Operators reduces Maximum Training Error Bounds. - Maier, Andreas; Syben-Leisner, Christopher; Stimpel, Bernhard; Würfl, Tobias; Hoffmann, Mathis; Schebesch, Frank; Fu, Weilin; Mill, Leonid; Kling, Lasse; Christiansen, Silke H. (Nature Machine Intelligence, 2019)
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.

MR to X-ray Projection Image Synthesis - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Mentl, Katrin; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.mrtoxr_0

Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning - Breininger, Katharina; Würfl, Tobias; Kurzendorfer, Tanja; Albarqouni, Shadi; Pfister, Marcus; Kowarschik, Markus; Navab, Nassir; Maier, Andreas (Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018)
For endovascular aortic repair (EVAR), integrating preoperative information of the aortic anatomy with intraoperative fluoroscopy can aid in reducing radiation exposure, contrast agent and procedure time. However, the quality of this fusion may deteriorate over the course of the intervention due to patient movement or deformation of the vasculature caused by interventional tools. Automatically detecting the instruments present in the X-ray image can help to assess the degree of deterioration, trigger automatic re-registration or aid in automatic workflow phase detection and process modeling. In this work, we investigate a flexible approach to segment different devices based on fully convolutional neural networks using multi-task learning. We evaluate the proposed approach on a set of 38 X-ray images acquired during EVAR interventions by targeting the segmentation of aortic stents, stiff guidewires and pigtail catheters. We compare the results to the performance of single-task networks. We manage to keep similar performance compared to single-task networks with Dice coefficients between 0.95 and 0.80 depending on the device, while speeding up computation by a factor of two.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.multip
Keywords: Catheters; Convolutional Neural Networks; Endovascular Aortic Repair; Guidewires; Multi-task learning; Stents; X-ray

Optimizing R with SparkR on a commodity cluster for biomedical research - Sedlmayr, Martin; Würfl, Tobias; Maier, Christian; Haeberle, Lothar; Fasching, Peter; Prokosch, Hans-Ulrich; Christoph, Jan (Computer Methods and Programs in Biomedicine, 2016)
Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department.For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity.The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication.R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution.
EVALuna2:174

Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-Site Robustness Assessment - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), 2018)
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accura

Physical Constraints for Beam Hardening Reduction using Polynomial Models - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.physic

Precision Learning: Reconstruction Filter Kernel Discretization - Syben-Leisner, Christopher; Stimpel, Bernhard; Breininger, Katharina; Würfl, Tobias; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.precis_0

Precision Learning: Towards Use of Known Operators in Neural Networks - Maier, Andreas; Schebesch, Frank; Syben-Leisner, Christopher; Würfl, Tobias; Steidl, Stefan; Choi, Jang-Hwan; Fahrig, Rebecca (2018 24rd International Conference on Pattern Recognition (ICPR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.precis_5

Projection image-to-image translation in hybrid x-ray/MR imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Lommen, Jonathan; Dörfler, Arnd; Maier, Andreas (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding x-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating x-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
CRIS-Team Scopus Importer:2019-07-12

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (Scientific Reports, 2019)
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.<br

Projective Reconstruction of Acquisition Geometry without Point Correspondences - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)
Despite some fundamental differences in the image formation model 3D Reconstruction in the fields of Computed Tomography and Computer Vision share some similar concepts. In Computed Tomography, reconstruction refers to the problem of estimating 3D structure from 2D projection images assuming that accurate information about the camera geometry is available while in Computer Vision reconstruction typically refers to the task of simultaneously estimating the 3D structure and the camera motion solely from 2D projection images. In Computed Tomography accurate information about the camera geometry or motion is usually acquired by calibration and scanners are manufactured to support reproducible trajectories. Being able to reconstruct without requiring calibration and a reproducible trajectory would solve the problem of rigid motion estimation and enable new types of scanners. However, it is difficult to transfer Computer Vision techniques to Computed Tomography because point correspondences are hard to establish since all structures are superimposed over each other in projection images. Recently we introduced a new algorithm two estimate pairwise relative geometry based solely on the 2D projection images without point correspondences. In this work, we present the adaptation of a structure from motion algorithm to obtain a projective reconstruction of the whole scene from pairwise geometry estimates. In a simulation experiment, we show that we are able to estimate the camera geometry only based on projections.<br

Realistic Image Synthesis of Imperfect Specimens using Generative Networks - Neufeld, Deniz; Würfl, Tobias; Gruber, Roland; Schön, Tobias; Maier, Andreas (Proceedings of the 9th Conference on Industrial Computed Tomography (iCT 2019), 2019)
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.realis

Reconstruction of Voxels with Position- and Angle-Dependent Weightings - Felsner, Lina; Würfl, Tobias; Syben-Leisner, Christopher; Roser, Philipp; Preuhs, Alexander; Maier, Andreas; Riess, Christian (The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments - Kedilioglu, Oguz; Lieret, Markus; Schottenhamml, Julia; Würfl, Tobias; Blank, Andreas; Maier, Andreas; Franke, Jörg (16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021), 2021)

Some Investigations on Robustness of Deep Learning in Limited Angle Tomography - Huang, Yixing; Würfl, Tobias; Breininger, Katharina; Liu, Ling; Lauritsch, Günter; Maier, Andreas (Proceedings of the 21st International Conference on Medical Image Computing & Computer Assisted Intervention, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.somein
Keywords: Deep learning; limited angle tomography; robustness; adversarial example

Stereo Rectification for X-ray Data Consistency Conditions - Aichert, André; Jérôme, Lesaint; Würfl, Tobias; Clackdoyle, Rolf; Desbat, Laurent; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.stereo

Towards Arbitrary Noise Augmentation-Deep Learning for Sampling from Arbitrary Probability Distributions - Horger, Felix; Würfl, Tobias; Christlein, Vincent; Maier, Andreas (MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018)
CRIS-Team WoS Importer:2019-08-09

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Communications in Computer and Information Science, 2019)
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated. The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.
CRIS-Team Scopus Importer:2019-09-17
Keywords: Confocal Laser Endomicroscopy; Head and neck squamous cell carcinoma; Transfer learning

Tutorial: Deep Learning - Advancing the State-of-the-Art in Medical Image Analysis - Christlein, Vincent; Ghesu, Florin-Cristian; Würfl, Tobias; Maier, Andreas; Isensee, Fabian; Neher, Peter; Maier-Hein, Klaus (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.tutori

U-Net for SPECT Image Denoising - Reymann, Maximilian; Würfl, Tobias; Stimpel, Bernhard; Ritt, Philipp; Cachovan, Michal; Vija, A. Hans; Maier, Andreas (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), 2019)
Single Photon Emitted Computed Tomography (SPECT) is characterized by low photon counts and high degrees of image noise. In this work we investigate deep image postprocessing methods for improving image quality in SPECT projections and propose a new architecture. Specifically, the residual U-Net proposed by Heinrich et al. for low-dose Computed Tomography (CT) and the Convolutional Denoising Autoencoder (CNN DAE) by Gondara et al. were tested for their performance for denoising SPECT projections. Reference images without noise and scatter were obtained from SPECT Monte Carlo simulations of 24 different XCAT phantoms of brains, lungs, livers and skeletons using the SIMIND software. These clean images were then used as training target for the neural networks. Additionally, we propose a U-Net architecture that is more suited for SPECT image denoising, consisting of 4 layers, each with 2 blocks of convolution, batch normalization and ReLU. Our results showed that the proposed U-Net outperforms the residual U-Net and the CNN DAE with a PSNR of 38.25 dB, compared to 27.95 dB and 28.25 dB, respectively. It was shown that the characteristics of the SPECT data posed a challenge to the neural networks, as there are only few distinct photon count values at the detector available and these are superimposed with a high degree of image noise, compared to low-dose CT. Our proposed network was able to decrease the noise within the phantom and additionally decreased the background noise in the image. The image quality was improved by 14.15 dB compared to the noisy input image, demonstrating the suitability of our network. We believe that with increased size and diversity of our dataset we can further boost the performance.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Unsupervised OCT Denoising using speckle split - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Hohberger, Bettina; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022)
CRIS-Team WoS Importer:2022-09-30