Respiratory Motion Compensation for C-arm CT Liver Imaging - Sindel, Aline; Bögel, Marco; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.respir

Respiratory Motion Compensation in Rotational Angiography: Graphical Model-Based Optimization of Auto-Focus Measures - Unberath, Mathias; Taubmann, Oliver; Bier, Bastian; Geimer, Tobias; Hell, Michaela; Achenbach, Stephan; Maier, Andreas (IEEE International Symposium on Biomedical Imaging, Proceedings of the, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.respir_2
Keywords: Angiography; Cardiac imaging; Cone-beam CT; Discrete optimization; Markov random fields

Respiratory Motion Compensation in X-Ray Fluoroscopy - Fischer, Peter (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Fluoroscopy is a common imaging modality in medicine for guidance of minimally invasive interventions due to its high temporal and spatial resolution and the good visibility of interventional devices and bones. To counteract its lack of 3-D information and soft-tissue contrast, the X-ray images can be enhanced with overlays. This constitutes a medical application of augmented reality technology. Most commonly, the overlays are static. Due to inevitable respiratory and cardiac motion of the patient when imaging chest or abdomen, the images and the overlays frequently are inconsistent. In this thesis, two methods for compensating this involuntary motion are presented. In the first method, a respiratory signal is estimated from a 2-D+t X-ray image sequence using unsupervised learning. The robustness of respiratory signal extraction to common disturbances occurring in interventional imaging is increased by patchbased processing and illumination invariance. The respiratory signal is used as prior information in the subsequent motion estimation step. Due to transparency effects in X-ray, conventional registration methods are not applicable for motion estimation. Thus, a novel surrogate-driven layered motion model is proposed to estimate the respiratory motion from the X-ray sequence. The motion model is incorporated in an energy formulation that is solved with an efficient graphics processing unit (GPU) implementation of primal-dual optimization. In the second method, pre-operative magnetic resonance imaging (MRI) enables 3-D imaging with good soft-tissue contrast. Real-time 2-D+t slice images are acquired and stacked to 3-D+t volumes using a Markov random field (MRF). The MRF enforces similar surrogate signals in slices that are assigned to each other as well as temporal smoothness of the assignment. The surrogate signal for respiratory motion is estimated from the MRI images, while the cardiac surrogate signal is derived from electrocardiography (ECG). In the MRI volumes, conventional 3-D/3-D registration is used to estimate the patient motion. In both methods, the surrogate signals and the estimated motions are combined in a motion model, which is then used for motion compensation in X-ray. For evaluation, experiments are conducted on pig and patient data. Compared to static overlays, the residual apparent motion in the X-ray images is reduced by 13% using the X-ray-based motion model and by 40% using the MRI-based motion model. The runtime of applying the motion model during the procedure is sufficient for real-time processing at common fluoroscopy frame rates. Two alternative methods with different properties are proposed in this thesis. Xray-based motion compensation requires no pre-procedural processing, but is more complex during the procedure and is limited to respiratory motion. MRI-based motion compensation can also handle cardiac motion, but needs to be transferred from MRI to X-ray. The choice of motion model depends on the requirements of the clinical application. To this end, the X-ray-based motion model is implemented in a prototype and is evaluated clinically.<br
Keywords: dimensionality reduction; motion compensation; motion model; respiratory signal; x-ray fluoroscopy

Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study - Bögel, Marco; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca; Britzen, Stefan; Maier, Andreas (International Journal of Biomedical Imaging, 2013)

Respiratory Motion Estimation using a 3D Diaphragm Model - Bögel, Marco; Riess, Christian; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung in der Medizin 2014, 2014)
Long acquisition times of several seconds lead to image artifacts in cardiac C-arm CT. These artifacts are mostly caused by respiratory motion. In order to improve image quality, it is important to accurately estimate the breathing motion that occurred during image acquisition. It has been shown that diaphragm motion is correlated to the respiration-induced motion of the heart. We present a method to estimate an accurate three-dimensional (3D) model of the diaphragm and its compression motion field from a set of C-arm CT projection images acquired during free breathing. First results on the digital XCAT phantom are promising. The method is able to estimate the motion field amplitude exactly. The boundaries of the estimated compression motion field are estimated within 3mm accurac
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.respir_33

Respiratory Motion Influence on Catheter Contact Force During Radio Frequency Ablation Procedures - Koch, Martin; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert (SPIE Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)

Respiratory Self-Navigation for Whole-Heart Coronary Magnetic Resonance Imaging - Piccini, Davide (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
As the average life span of the world population increases, cardiovascular diseases firmly establish themselves as the most frequent cause of death in many of the developed countries. Coronary artery disease (CAD) is responsible for more than half of these cases and there is, hence, a strong need for a non-invasive and radiation-free test that could be reliably adopted for its assessment in clinical routine. Although coronary magnetic resonance imaging (MRI) has always been regarded with high expectations, it is still not considered for clinical assessment of CAD. This is mainly due to several limitations of current coronary MRI examinations. The complex anatomy of the coronary arteries requires extensive scout-scanning to precisely plan the actual data acquisition. The current speed limitations of the MRI scanners and the contribution of cardiac and respiratory motion do not allow the high resolution acquisitions to be performed within the fraction of a single heartbeat. Consequently, data acquisition must be split into multiple heartbeats and usually performed during free-breathing. At the same time, gating with respect to a consistent respiratory position is applied using an interleaved navigated scan which monitors the position of the subject's diaphragm. Major improvements in standard navigator-gated free-breathing coronary MRI have been achieved in recent years, but a number of important intrinsic limitations, such as the prolonged and unknown acquisition times, the non-linearity of the motion compensation, and the complexity of the examination setup have so far hindered the clinical usage of this technique. In contrast, a technique known as self-navigation, which performs motion detection and correction solely based on imaging data of the heart, promises a priori knowledge of the duration of the acquisition with improved accuracy of the motion compensation and requires minimal expertise for the planning of the examination. In this work, a novel acquisition and motion correction strategy for free-breathing self-navigated whole-heart coronary MRA was introduced, analyzed and implemented to be entirely integrated in a clinical MR scanner. The proposed acquisition method consists of a novel interleaved 3D radial trajectory, mathematically constructed on the basis of a spiral phyllotaxis pattern, which intrinsically minimizes the eddy currents artifacts of the balanced steady state free-precessing acquisition, while ensuring a complete and uniform coverage of k-space. The self-navigated respiratory motion detection is performed on imaging readouts oriented along the superior-inferior axes and is based on a method for the isolation and automatic segmentation of the bright signal of the blood pool. Motion detection of the segmented blood pool is then performed using a cross-correlation technique. This fully automated respiratory selfnavigated method offers an easy and robust solution for coronary MR imaging that can also be integrated into a regular clinical routine examination. The technique was tested in volunteers, compared to the standard navigator-gating approach, and, for the first time to the author's knowledge, allowed self-navigation to be positively applied to a large patient study in an advanced clinical setting.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.respir_3
Keywords: coronary MRI; motion compensation; self-navigation

Restoration of Missing Data in Limited Angle Tomography Based on Consistency Conditions - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Hornegger, Joachim; Lauritsch, Günter; Maier, Andreas (The 14th International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
In limited angle tomography, only a limited angular range of data is acquired and consequently a double wedge-shaped region in the frequency domain representation of the imaged object is missing. Hence, streak artifacts occur. To restore the missing data, we perform a regression and an image fusion in sinogram domain and frequency domain, respectively. We first convert the sinogram restoration problem into a regression problem based on the Helgason-Ludwig consistency conditions. Due to the severe ill-posedness of the problem, regression only partially recovers the correct frequency components, especially lower frequency components, and will introduce erroneous ones, particularly higher frequencies. Bilateral filtering is utilized to retain the most prominent high frequency components and suppress erroneous ones. A fusion of the filtered image and the image reconstructed from the limited angle sinogram is performed afterwards in the frequency domain. The proposed method is evaluated on the Shepp-Logan phantom, for which the root-mean-square error of the reconstructed image decreases from 310 HU to 136 HU.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.restor_2
Keywords: limited angle tomography; data consistency condition; Helgason-Ludwig consistency condition

Restoration of Missing Data in Limited Angle Tomography Based on Helgason-Ludwig Consistency Conditions - Huang, Yixing; Huang, Xiaolin; Taubmann, Oliver; Xia, Yan; Haase, Viktor; Hornegger, Joachim; Lauritsch, Günter; Maier, Andreas (Biomedical Physics and Engineering Express, 2017)
In limited angle tomography, missing data in an insufficient angular scan will cause streak artifacts in the reconstructed images. Correspondingly, in the frequency domain representation of the imaged object, a double wedge-shaped region is missing. In this paper, we perform a regression in sinogram domain and an image fusion in frequency domain to restore the missing data. We first convert the sinogram restoration problem into a regression problem based on the Helgason–Ludwig consistency conditions. Due to its severe ill-posedness, regression only partially recovers the correct frequency components, especially lower frequency components, and will introduce erroneous ones, particularly higher frequencies. Bilateral filtering is utilized to retain the most prominent high frequency components and suppress erroneous ones. Afterwards, a fusion in the frequency domain utilizes the restored frequency components to fill the missing double wedge region. The proposed method is evaluated in a parallel-beam study on both numerical and clinical phantoms. The root-mean-square errors of the reconstructed images decrease from 302 to 78 HU for the noise-free Shepp–Logan phantom, from 355 to 175 HU for the noisy Shepp–Logan phantom, and from 187 to 56 HU for the clinical data. The results show that our method is promising in streak reduction and intensity offset compensation in both noise-free and noisy situations.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.restor
Keywords: limited angle tomography; Helgason-Ludwig consistency conditions; streak artifacts; regression; fusion

Retina Image Analysis System for Glaucoma Detection - Bock, Rüdiger; Meier, Jörg; Nyul, Laszlo Gabor; Michelson, Georg; Hornegger, Joachim (Biomedizinische Technik, BMT 2007, 2007)

Retinal blood flow measurement with ultrahigh-speed swept-source / Fourier domain optical coherence tomography - Baumann, Bernhard; Potsaid, Benjamin; Liu, Jonathan J.; Kraus, Martin; Huang, David; Hornegger, Joachim; Duker, Jay S.; Fujimoto, James G. (Ophthalmic Technologies XXI, 2011)
Doppler OCT is a functional extension of OCT that provides information on flow in biological tissues. We present a novel approach for total retinal blood flow assessment using ultrahigh speed Doppler OCT. A swept source / Fourier domain OCT system at 1050 nm was used for 3D imaging of the human retina. The high axial scan rate of 200 kHz allowed measuring the high flow velocities in the central retinal vessels. By analyzing en-face images extracted from 3D Doppler data sets, absolute flow for single vessels as well as total retinal blood flow can be measured using a simple and robust protocol. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography - Huang, Zhiyu; Jiang, Zhe; Hu, Yicheng; Zou, Da; Yu, Yue; Ren, Qiushi; Liu, Gangjun; Lu, Yanye (Biomedical Optics Express, 2020)
A multispectral fundus camera (MSFC), as a novel noninvasive technology, uses an extensive range of monochromatic light sources that enable the view of different sectional planes of the retinal and choroidal structures. However, MSFC imaging involves complex processes affected by various factors, and the recognized theory based on light absorption above the choroid is not sufficient. In an attempt to supplement the relevant explanations, in this study, we used optical coherence tomography (OCT), a three-dimensional tomography modality, to analyze MSFC results at the retina and choroid. The swept-source OCT system at 1060 nm wavelength with a 200 kHz A-scan rate and an MSFC with 11 bands at 470 to 845 nm are employed. A quantitative evaluation procedure is proposed to compare MSFC and OCT en face images. The comparative study shows that 1) the MSFC images with the illumination wavelength of less than 605 nm could mainly provide the retinal structure information; 2) Relative choroidal layer thickness information could be inferred from the MSFC images, especially the image acquiring under the wavelength more than 605 nm. According to the results, further investigation revealed the contribution of the perivascular tissue and the sclera scattering in the difference of vascular brightness in MSFC images. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
CRIS-Team WoS Importer:2020-10-30

Retinal image quality assessment based on image clarity and content - Abdel-Hamid, Lamiaa; El-Rafei, Ahmed; El-Ramly, Salwa; Michelson, Georg; Hornegger, Joachim (Journal of Biomedical Optics, 2016)
EVALuna2:21281

Retinal image synthesis from multiple-landmarks input with generative adversarial networks - Yu, Zekuan; Xiang, Qing; Meng, Jiahao; Kou, Caixia; Ren, Qiushi; Lu, Yanye (Biomedical Engineering Online, 2019)
Background: Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial networks (GANs) to enlarge training datasets for facilitating medical image analysis. For instance, conventional methods such as image-to-image translation techniques are used to synthesize fundus images with their respective vessel trees in the field of fundus image.
CRIS-Team WoS Importer:2019-06-07

Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients - Mayer, Markus Anton; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf-Peter (Biomedical Optics Express, 2010)
Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 μm was achieved on glaucomatous eyes, and 3.6 μm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 μm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis. © 2010 Optical Society of America.

Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database - Odstrcilik, Jan; Kolar, Radim; Budai, Attila; Hornegger, Joachim; Jan, Jiri; Gazarek, Jiri; Kubena, Tomas; Cernosek, Pavel; Svoboda, Ondrej; Angelopoulou, Elli (IET Image Processing, 2013)
Automatic assessment of retinal vessels plays an important role in the diagnosis of various eye, as well as systemic diseases. A public screening is highly desirable for prompt and effective treatment, since such diseases need to be diagnosed at an early stage. Automated and accurate segmentation of the retinal blood vessel tree is one of the challenging tasks in the computer-aided analysis of fundus images today. We improve the concept of matched filtering, and propose a novel and accurate method for segmenting retinal vessels. Our goal is to be able to segment blood vessels with varying vessel diameters in high-resolution colour fundus images. All recent authors compare their vessel segmentation results to each other using only low-resolution retinal image databases. Consequently, we provide a new publicly available high-resolution fundus image database of healthy and pathological retinas. Our performance evaluation shows that the proposed blood vessel segmentation approach is at least comparable with recent state-of-the-art methods. It outperforms most of them with an accuracy of 95% evaluated on the new database.

Retrospective fusion of F-18-FDG-PET and CT: additional diagnostic information in melanoma patients - Römer, Wolfgang; Nömayr, Anton; Greess, Holger; Fiedler, Ekkehard; Platsch, Günter; Pfahlberg, Annette; Hothorn, Torsten; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Eur J Nucl Med Mol Imaging, 2004)

Retrospective fusion of F-18-FDG-PET and CT: additional diagnostic information in melanoma patients. - Römer, Wolfgang; Nömayr, Anton; Greess, Holger; Fiedler, Ekkehard; Platsch, Günter; Pfahlberg, Annette; Hothorn, Torsten; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Philadelphia J Nucl Med, 2004)

Retrospective interactive rigid fusion of 18F-FDG-PET and CT: Additional diagnostic information in melanoma patients - Römer, Wolfgang; Nömayr, Anton; Greess, Holger; Fiedler, Ekkehard; Platsch, Günter; Schuler-Thurner, Beatrice; Pfahlberg, Annette; Hothorn, Torsten; Schuler, Gerold; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Nuklearmedizin-Nuclear Medicine, 2006)
Aim: This study investigates whether interactive rigid fusion of routine PET and CT data improves localization, detection and characterization of lesions compared to separate reading. For this purpose, routine PET and CT scans of patients with metastases from malignant melanoma were used. Patients, methods: In 34 patients with histologically confirmed malignant melanoma, FDG-PET and spiral CT were performed using clinical standard protocols. For all of these patients, gold standard was available. Clinical and radiological follow-up identified 82 lesions as definitely pathological. Two board-certified nuclear medicine physicians and two board-certified radiologists analyzed PET and CT images independently from each other. For each patient up to 32 anatomical regions (24 lymph node regions, 8 extranodular regions) were systematically classified. Discordant areas were interactively analyzed in manually and rigidly registered images using a commercially available fusion tool. No side-by-side reading was performed. Results: Image fusion disclosed that the evaluation of the PET images alone led to a mislocalization in 26 of 91 focally FDG enhancing lesions. The overall sensitivities of PET, CT, and image fusion were 85, 88, and 94%, respectively; the overall specificities of PET, CT and image fusion were 98, 95 and 100%, respectively. Image fusion exhibited statistically significant higher specificity values as compared with CT. Ten definitely malignant sites were false-negative in CT, but could be detected by PET. On the other hand, twelve metastases were false-negative in PET, but could be detected by CT. These included two lesions, which had a clear correlate on the PET image when the fused images were evaluated. On the whole, registration of the PET and CT images yielded additional diagnostic information in 44% of the definitely malignant lesions. Conclusion: Retrospective image fusion of independently obtained PET and CT data is particularly valuable in exactly lodizing foci of abnormal FDG uptake and improves the detection of metastases of malignant melanoma. © 2006 Schattauer GmbH.

Revisiting Log-Structured File Systems for Low-Power Portable Storage - Weißel, Andreas; Scherl, Holger; Bellosa, Frank; et al. (Proceedings of the International Workshop on Software Support for Portable Storage, 2005)
UnivIS-Import:2015-04-16:Pub.2005.tech.IMMD.IMMD4.revisi
Keywords: file system; hard disk; power management

Rewiring of neuronal networks during synaptic silencing - Wrosch, Jana Katharina; Von Einem, Vicky; Breininger, Katharina; Dahlmanns, Marc; Maier, Andreas; Kornhuber, Johannes; Groemer, Teja Wolfgang (Scientific Reports, 2017)
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.
EVALuna2:33558

Rigid and Non-Rigid Surface Registration for Range Imaging Applications in Medicine - Bauer, Sebastian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
The introduction of low-cost range imaging technologies that are capable of acquiring the three-dimensional geometry of an observed scene in an accurate, dense, and dynamic manner holds great potential for manifold applications in health care. Over the past few years, the use of range imaging modalities has been proposed for guidance in computer-assisted procedures, monitoring of interventional workspaces for safe robot-human interaction and workflow analysis, touch-less user interaction in sterile environments, and for application in early diagnosis and elderly care, among others. This thesis is concerned with the application of range imaging technologies in computer-assisted and image-guided interventions, where the geometric alignment of range imaging data to a given reference shape – either also acquired with range imaging technology or extracted from tomographic planning data – poses a fundamental challenge. In particular, we propose methods for both rigid and non-rigid surface registration that are tailored to cope with the specific properties of range imaging data. In the first part of this work, we focus on rigid surface registration problems. We introduce a point-based alignment approach based on matching customized local surface features and estimating a global transformation from the set of detected correspondences. The approach is capable of handling gross initial misalignments and the multi-modal case of aligning range imaging data to tomographic shape data. We investigate its application in image-guided open hepatic surgery and automatic patient setup in fractionated radiation therapy. For the rigid registration of surface data that exhibit only slight misalignments, such as with on-the-fly scene reconstruction using a hand-guided moving range imaging camera, we extend the classical iterative closest point algorithm to incorporate both geometric and photometric information. In particular, we investigate the use of acceleration structures for efficient nearest neighbor search to achieve real-time performance, and quantify the benefit of incorporating photometric information in endoscopic applications with a comprehensive simulation study. The emphasis of the second part of this work is on variational methods for non-rigid surface registration. Here, we target respiratory motion management in radiation therapy. The proposed methods estimate dense surface motion fields that describe the elastic deformation of the patient’s body. It can serve as a highdimensional respiration surrogate that substantially better reflects the complexity of human respiration compared to conventionally used low-dimensional surrogates. We propose three methods for different range imaging sensors and thereby account for the particular strengths and limitations of the individual modalities. For dense but noisy range imaging data, we propose a framework that solves the intertwined tasks of range image denoising and its registration with an accurate planning shape in a joint manner. For accurate but sparse range imaging data we introduce a method that aligns sparse measurements with a dense reference shape while simultaneously reconstructing a dense displacement field describing the non-rigid deformation of the body surface. For range imaging sensors that additionally capture photometric information, we investigate the estimation of surface motion fields driven by this complementary source of information.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.rigida
Keywords: range imaging; surface registration; rigid registration; non-rigid registration

RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting - Hoppe, Elisabeth; Thamm, Florian; Koerzdoerfer, Gregor; Syben-Leisner, Christopher; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (Bilderverarbeitung für die Medizin Algorithmen - Systeme - Anwendungen, 2020)

RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting - Hoppe, Elisabeth; Thamm, Florian; Körzdörfer, Gregor; Syben-Leisner, Christopher; Schirrmacher, Franziska; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (Proceedings of MICCAI 2019, 2019)
Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed CNNs for the MRF reconstruction reducing the error in T1 and T2 by more than 80%.<br

RITK: The Range Imaging Toolkit - A Framework for 3-D Range Image Stream Processing - Wasza, Jakob; Bauer, Sebastian; Haase, Sven; Schmid, M.; Reichert, S.; Hornegger, Joachim (VMV 2011: Vision, Modeling & Visualization, 2011)

Road to Net Zero. Strategic Pathways for Sustainability-Driven Business Transformation. - Zipse, Oliver; Hornegger, Joachim; Becker, Thomas; Beckmann, Markus; Bengsch, Michael; Feige, Irene; Schober, Markus (Springer, 2023)

Robust Automatic Evaluation of Intelligibility in Voice Rehabilitation Using Prosodic Analysis - Haderlein, Tino; Schützenberger, Anne; Döllinger, Michael; Nöth, Elmar (Proc. Text, Speech and Dialogue; 20th International Conference, TSD 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_5
Keywords: Automatic assessment; Intelligibility; Prosody; Reading errors

Robust blood flow velocity estimation from 3D rotational angiography - Bögel, Marco; Gehrisch, S.; Redel, T.; Rohkohl, C.; Birkhold, A.; Hölter, Philip; Maier, Andreas; Kowarschik, M.; Dörfler, Arnd (Proceedings - International Conference on Image Processing, ICIP, 2018)
Blood flow velocity estimation techniques from 2D fluoroscopy and more recently rotational angiography images represent a topic of wide interest in various clinical research areas. In particular, they can be an important step towards patient-specific flow simulations. Additionally, it can be of diagnostic interest to evaluate volumetric blood flow in stenotic vessel segments; e.g. in a patient's brain. In this work, we present a robust optimization-based approach to estimate mean blood flow velocities from rotational digital subtraction angiography (DSA) images. Our method was extensively evaluated on 70 simulated datasets and 6 clinical datasets with MR phase contrast ground truth data. Our evaluation explores the limitations of image-based velocity estimation; i.e., measurements over short or small vessel segments. Overall, we were able to estimate the mean velocity with average errors as little as 4% for simulation studies, if the vessel segment is sufficiently long, and achieved results within the confines of the MR phase contrast ground truth data for our clinical data, with an average relative error to the centerline measurement of 9.5%±10.5%. The achieved accuracy enables patient-specific hemodynamic simulations and may also be of immediate diagnostic interest.
CRIS-Team Scopus Importer:2022-06-05
Keywords: Angiography; Contrast medium; Flow quantification; Velocity estimation

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment - Gündel, Sebastian; Setio, Arnaud A.A.; Ghesu, Florin-Cristian; Grbic, Sasa; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (Medical Image Analysis, 2021)
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.
Keywords: label noise; robust loss function; multi-task learning; chest radiography abnormality classification

Robustes Echtzeit-Feedback für die gebundene, weiche Sprechtechnik in der Stottertherapie - Haderlein, Tino; Hönig, Florian Thomas; Jassens, Frank; Mahlberg, Lea; Nöth, Elmar; Wolff von Gudenberg, Alexander (Aktuelle phoniatrisch-pädaudiologische Aspekte 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.robust_91

Robust graphene membranes in a silicon carbide frame - Waldmann, Daniel; Butz, Benjamin; Bauer, Sebastian; Englert, Jan; Jobst, Johannes; Ullmann, Konrad; Fromm, Felix; Ammon, Maximilian Michael; Enzelberger-Heim, Michael; Hirsch, Andreas; Maier, Sabine; Schmuki, Patrik; et al. (ACS nano, 2013)
We present a fabrication process for freely suspended membranes consisting of bi- and trilayer graphene grown on silicon carbide. The procedure, involving photoelectrochemical etching, enables the simultaneous fabrication of hundreds of arbitrarily shaped membranes with an area up to 500 μm(2) and a yield of around 90%. Micro-Raman and atomic force microscopy measurements confirm that the graphene layer withstands the electrochemical etching and show that the membranes are virtually unstrained. The process delivers membranes with a cleanliness suited for high-resolution transmission electron microscopy (HRTEM) at atomic scale. The membrane, and its frame, is very robust with respect to thermal cycling above 1000 °C as well as harsh acidic or alkaline treatment.

Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray Images - Bachmaier, Magdalena; Rohleder, Maximilian; Swartman, Benedict; Privalov, Maxim; Maier, Andreas; Kunze, Holger (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Standardized image rotation is essential to improve reading performance in interventional X-ray imaging. To minimize user interaction and streamline the 2D imaging workflow, we present a new automated image rotation method. Image rotation can follow two steps: First, an anatomy specific centerline image is predicted which depicts the desired anatomical axis to be aligned vertically after rotation. In a second step, the necessary rotation angle is calculated from the orientation of the predicted line image. We propose an end-to-end trainable model with the Hough transform (HT) and a differentiable spatial-to-angular transform (DSAT) embedded as known operators. This model allows to robustly regress a rotation angle while maintaining an explainable inner structure and allows to be trained with both a centerline segmentation and angle regression loss. The proposed method is compared to a Hu moments-based method on anterior-posterior X-ray images of spine, knee, and wrist. For the wrist images, the HT based method reduces the mean absolute angular error (MAE) from 9. 28 ∘ using the Hu moments-based method to 3. 54 ∘. Similar results for the spinal and knee images can be reported. Furthermore, a large improvement of the 90 th percentile of absolute angular error by a factor of 3 indicates a better robustness and reduction of outliers for the proposed method.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Hough Transform; Image Rotation; Machine Learning

Robust Identification of Contrasted Frames in Fluoroscopic Images - Hoffmann, Matthias; Müller, Simone; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.robust_3

Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data - Neumann, Dominik; Tommaso, Mansi; Georgescu, Bogdan; Kamen, Ali; Kayvanpour, Elham; Amr, Ali; Sedaghat-Hamedani, Farbod; Haas, Jan; Katus, Hugo; Meder, Benjamin; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, 2014)
Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patient's physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.robust_8

Robust lumen segmentation of coronary arteries in 2D angiographic images - Polyanskaya, Maria; Schwemmer, Chris; Linarth, Andre Guilherme; Lauritsch, Günter; Hornegger, Joachim (Medical Imaging 2012: Image Processing, 2012)
Diagnosis and treatment of coronary diseases depends on the data acquired during angiographic investigations. To provide better assistance for angiographic procedures, a segmentation of the lumen is required. A new algorithm for vessel centerline computation and lumen segmentation in 2D projection coronary angiograms is presented. Centerlines are extracted by a graph-based optimization technique, which searches for paths with minimal costs. The search starts from a source point, which is automatically set by the proposed algorithm. A new objective function for determining the costs of the graph edges is proposed. It consists of the response from the medialness filter and is regularized by the centerline potential function. In the medialness filter a vessel cross-section is represented by a 1D profile parameterized by center position and radius. The medialness filter at a point optimizes a gradient-based response over the profile radius. The proposed centerline potential function defines likeliness of each point of the image to be a centerline. Both the medialness filter and the centerline potential function are multi-scale. The entire lumen segmentation is achieved by the radii extracted during the medialness response computation. Application to clinical data shows that the presented algorithm segments coronary lumen with good accuracy and allows for subsequent assessment of the quantitative characteristics (i.e. diameter, curvature, etc.) of the vessels. © 2012 SPIE.

Robust mixed one-bit compressive sensing - Huang, Xiaolin; Yang, Haiyan; Huang, Yixing; Shi, Lei; He, Fan; Maier, Andreas; Yan, Ming (Signal Processing, 2019)
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in conventional signal recovery methods. Aiming at acquiring information from noisy saturated and regular measurements, we in this paper propose a new signal recovery method called mixed one-bit compressive sensing (M1bit-CS) and develop an efficient algorithm in the framework of alternating direction methods of multipliers. Numerical experiments on one-dimensional symmetric signals and two-dimensional image reconstruction from computed tomography verify the effectiveness of M1bit-CS on signal recovery from saturated measurements.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Compressive sensing; Image reconstruction; One-bit; Signal recovery

Robust Model-based 3D/3D Fusion using Sparse Matching for Minimally Invasive Surgery - Neumann, Dominik; Grbic, Sasa; John, Matthias; Navab, Nassir; Hornegger, Joachim; Ionasec, Razvan (MICCAI 2013, 2013)

Robust Multiframe Super-Resolution Employing Iteratively Re-Weighted Minimization - Köhler, Thomas; Huang, Xiaolin; Schebesch, Frank; Aichert, André; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Computational Imaging, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.robust

Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_1

Robust Multi-View 2-D/3-D Registration Using Point-To-Plane Correspondence Model - Schaffert, Roman; Wang, Jian; Fischer, Peter; Maier, Andreas; Borsdorf, Anja (IEEE Transactions on Medical Imaging, 2020)
In minimally invasive procedures, the clinician relies on image guidance to observe and navigate the operation site. In order to show structures which are not visible in the live X-ray images, such as vessels or planning annotations, X-ray images can be augmented with pre-operatively acquired images. Accurate image alignment is needed and can be provided by 2-D/3-D registration. In this paper, a multi-view registration method based on the point-to-plane correspondence model is proposed. The correspondence model is extended to be independent of the used camera coordinates and different multi-view registration schemes are introduced and compared. Evaluation is performed for a wide range of clinically relevant registration scenarios. We show for different applications that registration using correspondences from both views simultaneously provides accurate and robust registration, while the performance of the other schemes varies considerably. Our method also outperforms the state-of-the-art method for cerebral angiography registration, achieving a capture range of 18 mm and an accuracy of 0.22 & x00B1;0.07 mm. Furthermore, investigations on the minimum angle between the views are performed in order to provide accurate and robust registration, while minimizing the obstruction to the clinical workflow. We show that small angles around 30 & x00B0; are sufficient to provide reliable registration results.
CRIS-Team WoS Importer:2020-01-24

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization - Liu, Chang; Huang, Yixing; Maier, Joscha; Klein, Laura; Kachelriess, Marc; Maier, Andreas (Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization, 2020)
In computed tomography (CT), automatic exposure control (AEC) is frequently used to reduce radiation dose exposure to patients. For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction. In this work, we investigate the performance of automated transform by manifold approximation (AUTOMAP) in such applications. For proof of concept, we investigate its performance on the MNIST dataset first, where the dataset containing all the 10 digits are randomly split into a training set and a test set. We train the AUTOMAP model for image reconstruction from 2 projections or 4 projections directly. The test results demonstrate that AUTOMAP is able to reconstruct most digits well with a false rate of 1.6% and 6.8% respectively. In our subsequent experiment, the MNIST dataset is split in a way that the training set contains 9 digits only while the test set contains the excluded digit only, for instance "2". In the test results, the digit "2"s are falsely predicted as "3" or "5" when using 2 projections for reconstruction, reaching a false rate of 94.4%. For the application in medical images, AUTOMAP is also trained on patients' CT images. The test images reach an average root-mean-square error of 290 HU. Although the coarse body outlines are well reconstructed, some organs are misshaped.<br
Keywords: Sparse-view reconstruction; deep learning; automatic exposure control; computed tomography

Robust Non-Rigid Registration through Agent-Based Action Learning - Krebs, Julian; Mansi, Tommaso; Delingette, Herve; Zhang, Li; Ghesu, Florin-Cristian; Miao, Shun; Maier, Andreas; Ayache, Nicholas; Liao, Rui; Kamen, Ali (Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_0

Robust Parallel Speech Recognition in Multiple Energy Bands - Maier, Andreas; Hacker, Christian; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich (Pattern Recognition, 27th DAGM Synopsium, Vienna, Austria, August/September 2005, Proceedings, 2005)

Robust Personalization of Cardiac Computational Models - Neumann, Dominik (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Heart failure (HF) is a major cause of morbidity and mortality in the Western world, yet early diagnosis and treatment remain a major challenge. As computational cardiac models are becoming more mature, they are slowly evolving into clinical tools to better stratify HF patients, predict risk and perform treatment planning. A critical prerequisite, however, is their ability to precisely capture an individual patient's physiology. The process of fitting a model to patient data is called personalization, which is the overarching topic of this thesis. An image-based, multi-physics 3D whole-heart model is employed in this work. It consists of several components covering anatomy, electrophysiology, biomechanics and hemodynamics. Building upon state-of-the-art personalization techniques, the first goal was to develop an automated pipeline for personalizing all components of the model in a streamlined and reproducible fashion, based on routinely acquired clinical data. Evaluation was performed on a multi-clinic cohort consisting of 113 patients, the largest cohort in any comparable study to date. The goodness of fit between personalized models and ground-truth clinical data was mostly below clinical variability, while a full personalization was finalized within only few hours. This showcases the ability of the proposed pipeline to extract advanced biophysical parameters robustly and efficiently. Designing such personalization algorithms is a tedious, model- and data-specific process. The second goal was to investigate whether artificial intelligence (AI) concepts can be used to learn this task, inspired by how humans manually perform it. A self-taught artificial agent based on reinforcement learning is proposed, which first learns how the model behaves, then computes an optimal strategy for personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters. The obtained results for two different models suggested that equivalent, if not better goodness of fit than standard methods could be achieved, while being more robust and with faster convergence rate. AI approaches could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Due to limited data, uncertainty in the clinical measurements, parameter non-identifiability, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. The third goal of this work was uncertainty quantification of the estimated parameters and to ascertain the uniqueness of the found solution. A stochastic method based on Bayesian inference and fast surrogate models is proposed, which estimates the posterior of the model, taking into account uncertainties due to measurement noise. Experiments on the biomechanics model showed that not only goodness of fits equivalent to the standard methods could be achieved, but also the non-uniqueness of the problem could be demonstrated and uncertainty estimates reported, crucial information for subsequent clinical assessments of the personalized models.<br
Keywords: Artificial Intelligence; Computational Modeling; Electromechanics; Heart; Inverse Optimization; Machine Learning; Personalization; Reinforcement Learning; Uncertainty Quantification

Robust Physically-Constrained Modeling of the Mitral Valve and Subvalvular Apparatus - Voigt, Ingmar; Mansi, Tommaso; Ionasec, Razvan Ioan; Assoumou Mengue, Etienne; Houle, Helene; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (Proceedings of the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, 2011)
Mitral valve (MV) is often involved in cardiac diseases, with various pathological patterns that require a systemic view of the entire MV apparatus. Due to its complex shape and dynamics, patient-specific modeling of the MV constitutes a particular challenge. We propose a novel approach for personalized modeling of the dynamic MV and its subvalvular apparatus that ensures temporal consistency over the cardiac sequence and provides realistic deformations. The idea is to detect the anatomical MV components under constraints derived from the biomechanical properties of the leaflets. This is achieved by a robust two-step alternate algorithm that combines discriminative learning and leaflet biomechanics. Extensive evaluation on 200 transesophageal echochardiographic sequences showed an average Hausdorff error of 5.1 mm at a speed of 9 sec, which constitutes an improvement of up to 11.5% compared to purely data driven approaches. Clinical evaluation on 42 subjects showed, that the proposed fully-automatic approach could provide discriminant biomarkers to detect and quantify remodeling of annulus and leaflets in functional mitral regurgitation.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.robust_1

Robust physically-constrained modeling of the mitral valve and subvalvular apparatus - Voigt, Ingmar; Tommaso, Mansi; Ionasec, Razvan; Assoumou Mengue, Etienne; Houle, Helene; Georgescu, Bogdan; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
Mitral valve (MV) is often involved in cardiac diseases, with various pathological patterns that require a systemic view of the entire MV apparatus. Due to its complex shape and dynamics, patient-specific modeling of the MV constitutes a particular challenge. We propose a novel approach for personalized modeling of the dynamic MV and its subvalvular apparatus that ensures temporal consistency over the cardiac sequence and provides realistic deformations. The idea is to detect the anatomical MV components under constraints derived from the biomechanical properties of the leaflets. This is achieved by a robust two-step alternate algorithm that combines discriminative learning and leaflet biomechanics. Extensive evaluation on 200 transesophageal echochardiographic sequences showed an average Hausdorff error of 5.1mm at a speed of 9sec, which constitutes an improvement of up to 11.5% compared to purely data driven approaches. Clinical evaluation on 42 subjects showed, that the proposed fully-automatic approach could provide discriminant biomarkers to detect and quantify remodeling of annulus and leaflets in functional mitral regurgitation. © 2011 Springer-Verlag.

Robust real-time 3D modeling of static scenes using solely a time-of-flight sensor - Feulner, Johannes; Penne, Jochen; Kollorz, Eva Nicole Karin; Hornegger, Joachim (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, 2009)
An algorithm is proposed for the 3D modeling of static scenes solely based on the range and intensity data acquired by a Time-of-Flight camera during an arbitrary movement. No additional scene acquisition devices, like inertia sensor, positioning robots or intensity based cameras are incorporated. The current pose is estimated by maximizing the uncentered correlation coefficient between edges detected in the current and a preceding frame at a minimum frame rate of four fps and an average accuracy of 45 mm. The paper also describes several extensions for robust registration like multiresolution hierarchies and projection Iterative Closest Point algorithm. The basic registration algorithm and its extensions were intensively evaluated against ground truth data to validate the accuracy, robustness and real-time-capability. © 2009 IEEE.

Robust Real-Time 3D Respiratory Motion Detection Using Time-of-Flight Cameras - Penne, Jochen; Schaller, Christian; Hornegger, Joachim; Kuwert, Torsten (International Journal of Computer Assisted Radiology and Surgery, 2008)
Purpose: Respiratory motion of the patient during data acquisition causes artifacts in the field of emission or computed tomography. Respiratory gating allows to track and correct these artifacts. Materials and methods: In this paper, we present a system that uses the fairly new and off-the-shelf time-of-flight (ToF) technology to compute a dense estimate of the three-dimensional respiratory motion of a patient. The work is characterized by three key contributions. The first is the employment of ToF sensors. Using ToF sensors, it is feasible to acquire a dense 3D surface model of the chest and abdomen of the patient with more than 15 frames per second. The second contribution is an algorithm to derive a surface representation which enables the estimation of the 3D respiratory motion of the patient, which is sufficient to compute 1D breathing signals for scalable specific regions of interest like chest and abdomen. The proposed data-driven algorithm models the chest and abdomen three-dimensionally by fitting distinct planes to different regions of the torso of the patient. The third contribution is the possibility to derive a sub-millimeter accurate 1D respiratory motion signal by observing the displacement of each plane. Results: Our ToF modeling approach enables marker less, real-time, 3D tracking of patient respiratory motion with an accuracy of 0.1 mm. Conclusion: Thus, our approach provides 1D breathing signals for scalable anatomical regions of interest with sufficient accuracy for artifact reduction in SPECT or X-ray angiography. © CARS 2008.
UnivIS-Import:2017-11-06:Pub.2008.tech.IMMD.IMMD5.robust_2

Robust real-time 3D time-of-flight based gesture navigation - Penne, Jochen; Soutschek, Stefan; Fedorowicz, Stefan; Hornegger, Joachim (2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008, 2008)
Contactless Human-Machine-Interfaces (HMIs) are an important issue in various applications where a haptic in-teraction with an input device is not possible or not appro-priate. Newly developed Time-of-Flight cameras provide 3D information of the observed scene in real-time at con-stant lateral resolutions of thousands of pixels. Addition-ally, a gray-value image of the observed scene is available. Our work compromises three major contributions: First, the robust and real-time capable segmentation of the hand by incorporating 3D and gray-value information; Second, the reliable classification of the performed static gesture using robust features; Third, the design of an HMI which uses the classified gesture as well as the 3D position of the hand to enable complex and convenient user interactions. The benefit of using a ToF camera is that the 3D information is just not available from classical 2D camera systems and thus only with ToF cameras the three dimensions of free-dom which are given for non-haptic interactions can be fully used. Currently, classification rates of 98.2% are achieved user-dependent and 94.3% user-independent for 6 gestures. Tests with untrained persons yielded a good to very good acceptance of the HMI. © 2008 IEEE.

Robust Real-Time 3D Time-of-Flight Based Gesture Navigation - Penne, Jochen; Soutschek, Stefan; Schaller, Christian; Hornegger, Joachim (Workshop Proceedings der Tagungen Mensch&Computer 2008, DeLFI 2008 und Cognitive Design 2008, 2008)

Robust Seed Mask Generation for Interactive Image Segmentation - Amrehn, Mario; Steidl, Stefan; Kowarschik, Markus; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist of drawing pictorial hints as an initial estimate of the object to extract. Only after this time consuming first phase, the efficient selective refinement of current segmentation results begins. Erroneously labeled seeds, especially near the border of the object, are challenging to detect and replace for a human and may substantially impact the overall segmentation quality. We propose an automatic seeding pipeline as well as a configuration based on saliency recognition, in order to skip the time-consuming initial interaction phase during segmentation. A median Dice score of 68.22% is reached before the first user interaction on the test data set with an error rate in seeding of only 0.088
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_6

Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging - Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kannengiesser, Stephan A. R.; Maier, Andreas; Hornegger, Joachim (18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II, 2015)
Fat quantification based on the multi-echo Dixon method is gaining importance in clinical practice as it can match the accuracy of spectroscopy but provides high spatial resolution. Accurate quantification protocols, though, are limited to low SNR and suffer from a high noise bias. As the clinically relevant water and fat components are estimated by fitting a non-linear signal model to the data, the uncertainty is further amplified. In this work, we first establish the low-rank property and its locality assumptions for water-fat MRI and, consequently, propose a model-consistent but adaptive spectral denoising. A robust noise estimation in combination with a risk-minimizing threshold adds to a fully-automatic method. We demonstrate its capabilities on abdominal fat quantification data from in-vivo experiments. The denoising reduces the fit error on average by 37% and the uncertainty of the fat fraction by 58% in comparison to the original data while being edge-preserving.
Keywords: Robust Denoising; Water-Fat MRI; Locally Low-Rank

Robust Vessel Segmentation in Fundus Images - Budai, Attila; Bock, Rüdiger; Maier, Andreas; Hornegger, Joachim; Michelson, Georg (International Journal of Biomedical Imaging, 2013)
One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods. © 2013 A. Budai et al.
UnivIS-Import:2015-03-09:Pub.2013.tech.IMMD.IMMD5.robust

ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration - Placht, Simon; Fürsattel, Peter; Assoumou Mengue, Etienne; Hofmann, Hannes; Schaller, Christian; Balda, Michael; Angelopoulou, Elli (Lecture Notes in Computer Science, 2014)
We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV's checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups. © 2014 Springer International Publishing.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.rochad
Keywords: Checkerboard Detection; Lens Distortion; Low Resolution Sensors; Multi Camera Calibration; Saddle-Based Subpixel Refinement

Rule-Based Standard Arabic Phonetization at Phoneme, Allophone, and Syllable Level - Sindran, Fadi; Mualla, Firas; Haderlein, Tino; Daqrouq, Khaled; Nöth, Elmar (International Journal of Computational Linguistics, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.ruleba
Keywords: Sprache

Run Surface Classification: a Digital Sports Embedded Application - Eskofier, Björn; Hornegger, Joachim; Oleson, Mark; Munson, Ian; DiBenedetto, Christian (Künstliche Intelligenz, 2008)

Salivary Markers for Quantitative Dehydration Estimation During Physical Exercise - Ring, Matthias; Lohmueller, Clemens; Rauh, Manfred; Mester, Joachim; Eskofier, Björn (IEEE Journal of Biomedical and Health Informatics, 2017)

Sampling rate impact on energy consumption of biomedical signal processing systems - Tobola, Andreas; Streit, Franz-Josef; Espig, Chris; Korpok, Oliver; Leutheuser, Heike; Sauter, Christian; Lang, Nadine; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; et al. (12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sampli

Scalable ECG Hardware and Algorithms for Extended Runtime of Wearable Sensors - Tobola, Andreas; Espig, Chris; Streit, Franz-Josef; Korpok, Oliver; Leutheuser, Heike; Schmitz, Björn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Eskofier, Björn; Fischer, Georg (2015 IEEE International Symposium on Medical Measurements and Applications, 2015)
Everything in nature tries to reach the lowest possible energy level. Therefore any natural or artificial system must have the ability to adjust itself to the changing requirements of its surrounding environment. In this paper we address this issue by an ECG sensor designed to be adjustable during runtime, having the ability to reduce the power consumption at cost of the informational content. Accessible for everyone, standard ECG hardware and open source software has been used to realize an ECG processing system for wearable applications. The average power consumption has been measured for each mode of operation. Finally we take conclusion to conciser context-aware scaling as key feature to address the energy issue of wearable sensor system
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.scalab

Scale-Space Anisotropic Total Variation for Limited Angle Tomography - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2018)
This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm, which is derived from wTV, in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2s neighboring pixels along the streaks’ normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks’ normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scale
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.scales
Keywords: limited angle tomography; total variation; scale-space; anisotropic

Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm - Xia, Yan; Dennerlein, Frank; Bauer, Sebastian; Hofmann, Hannes; Hornegger, Joachim; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose: Recently, a reconstruction algorithm for region of interest (ROI) imaging in C-arm CT was published, named Approximate Truncation Robust Algorithm for Computed Tomography (ATRACT). Even in the presence of substantial data truncation, the algorithm is able to reconstruct images without the use of explicit extrapolation or prior knowledge. However, the method suffers from a scaling and offset artifact in the reconstruction. Hence, the reconstruction results are not quantitative. It is our goal to reduce the scaling and offset artifact so that Hounsfield unit (HU) values can be used for diagnosis. Methods: In this paper, we investigate two variants of the ATRACT method and present the analytical derivations of these algorithms in the Fourier domain. Then, we propose an empirical correction measure that can be applied to the ATRACT algorithm, to effectively compensate the scaling and offset issue. The proposed method is evaluated on ten clinical datasets in the presence of different degrees of artificial truncation. Results: With the proposed correction approach, we achieved an average relative root-mean-square error (rRMSE) of 2.81 % with respect to non-truncated Feldkamp, Davis, and Kress reconstruction, even for severely truncated data. The rRMSE is reduced to as little as 10 % of the image reconstructed without the scaling calibration. Conclusions: The reconstruction results show that ROI reconstruction of high accuracy can be achieved since the scaling and offset artifact are effectively eliminated by the proposed method. With this improvement, the HU values may be used for post-processing operations such as bone or soft tissue segmentation if some tolerance is accepted. © 2014 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.scalin
Keywords: Dose reduction; Region of interest imaging; Scaling calibration; Truncation correction

Scaling Calibration in the ATRACT Algorithm - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Proceedings Bildverarbeitung für die Medizin 2013, 2013)

ScanRacer – Fast Track Annotation of Segmentation Masks in Volumetric Medical Images - Diehm, Jessica; Hermann, Robert; Pertlwieser, Tobias; Cheng, Wei; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (HEALTHINF - 14th International Conference on Health Informatics, 2021)
Introduction With the rise of deep learning [1], we see a dramatic need of curated and annotated medical image data. In particular, in volumetric images, such annotations are extremely costly, as each slice has to be outlined individually to generate ground truth for the training of deep learning algorithms. Recently, a new initiative was founded that aims at encouraging patients to donate their medical image data [2]. In particular, this initiative also asks for permission to crowd-source the data annotation. This forms a basis to generate sufficient data for large-scale training of deep learning algorithms in medical image analysis. Methods In order to encourage users to perform annotations, we explore gamification. In particular, we selected the setup of a racing game to generate an exciting user experience. The main idea is that the user is driving a race car across a volumetric image slice. In order to create tracks automatically, a simple segmentation method is required. For the first experiments, we chose thresholding to separate fore- and background. Then, we select the largest connected component and perform image processing to automatically extract a closed edge contour. As guidance, checkpoints are spread over the preliminary organ outline to guide the player in equidistant steps along the extracted contour. Based on edge detection [3], a score consisting of accumulated edge pixels is determined automatically as feedback for the player. In order to create a more challenging game experience, additional moving obstacles were added to the course. During driving the player creates a closed contour that is then transmitted from the game client to the server’s database. Results The game was implemented in Unity3D [4] and released as Android APK Installer (https://www.medicaldatadonors.org/index.php/scan-racer/). We chose Google Firebase to store segmentation results on the server (https://firebase.google.com). ScanRacer creates a challenging, yet rewarding experience for the user. Experienced players are able to create segmentation contours close to the correct segmentation outline. Yet detailed annotations still pose a challenge in this setup which will be addressed by the addition of further game elements. In contrast to many other serious games, the fun component of ScanRacer is very high as reported by test players. A gameplay teaser video demonstrates this in detail. (https://www.youtube.com/watch?v=JNmEGLCyf6w) Conclusion ScanRacer offers a unique experience for players for volumetric image segmentation. The game is was implemented in Unity3D and is available for free download. Sources can be shared at request. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [3] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698. [4] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press<br
Keywords: Gamification; Medical Image Annotation; Deep Learning

Scatter Correction for C-Arm CT Using Primary Modulation - Bier, Bastian; Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Ritschl, Ludwig; Kachelriess, Marc; Fahrig, Rebecca; Maier, Andreas (Proceedings of the 4th International Conference on Image Formation in X-ray Computed Tomography, 2016)
UnivIS-Import:2018-09-11:Pub.2016.tech.IMMD.IMMD5.scatte_8

Scatter correction using a primary modulator on a clinical angiography C-arm CT system - Bier, Bastian; Berger, Martin; Maier, Andreas; Kachelriess, Marc; Ritschl, Ludwig; Müller, Kerstin; Choi, Jang-Hwan; Fahrig, Rebecca (Medical Physics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.scatte
Keywords: C-Arm; CBCT; Primary Modulator; Scatter Correction

Scene Illumination as an Indicator of Image Manipulation - Riess, Christian; Angelopoulou, Elli (Information Hiding - 12th International Conference, 2010)
The goal of blind image forensics is to distinguish original and manipulated images. We propose illumination color as a new indicator for the assessment of image authenticity. Many images exhibit a combination of multiple illuminants (flash photography, mixture of indoor and outdoor lighting, etc.). In the proposed method, the user selects illuminated areas for further investigation. The illuminant colors are locally estimated, effectively decomposing the scene in a map of differently illuminated regions. Inconsistencies in such a map suggest possible image tampering. Our method is physics-based, which implies that the outcome of the estimation can be further constrained if additional knowledge on the scene is available. Experiments show that these illumination maps provide a useful and very general forensics tool for the analysis of color image

Schätzung von Faltungskernen für die Streusignalkorrektur in der Röntgenprojektionsradiographie aus mittels gewichteter Differenzen erzeugten Streustrahlbildern - Luckner, Christoph; Maier, Andreas; Dennerlein, Frank (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.schtzu
Keywords: scatter correction; projection; x-ray

Schlafstadienbestimmung mit Hilfe bewegungsbasierter Sensortechnologie - Traxdorf, Maximilian; Gradl, Stefan; Kugler, Patrick; Leutheuser, Heike; Lauten, Juliane; Eskofier, Björn; Angerer, Florian; Iro, Heinrich (Proceedings of the 84th Annual Meeting of the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery, 2013)

Scoring and Re-Ranking of ASR Hypotheses Using Phoneme Error Models - Hacker, Martin; Nöth, Elmar (to appear in: Proceedings of the 11. ITG Symposium on Speech Communication, 2014)
UnivIS-Import:2015-04-17:Pub.2014.tech.IMMD.lehstu.agdigi.scorin

Segmentation based features for lymph node detection from 3-D chest CT - Feulner, Johannes; Zhou, S. Kevin; Hammon, Matthias; Hornegger, Joachim; Comaniciu, Dorin (Machine Learning in Medical Imaging, 2011)
Lymph nodes routinely need to be considered in clinical practice in all kinds of oncological examinations. Automatic detection of lymph nodes from chest CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Features extracted from a segmentation that is initialized with a detection candidate can be used to train a classifier that decides whether the detection is a true or false positive. In this paper, the graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the candidate segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross validation on 54 CT datasets showed that the proposed system reaches a detection rate of 60.9% with only 6.1 false alarms per volume image, which is better than the current state of the art of mediastinal lymph node detection. © 2011 Springer-Verlag.

Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning - Schröter, Hendrik; Nöth, Elmar; Maier, Andreas; Cheng, Rachael; Barth, Volker; Bergler, Christian (ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater hydrophones and made it publicly available on the Orchive. There exist over 15 000 manually extracted orca/noise annotations and about 20 000 h unseen audio data. To analyze the behavior and communication of killer whales we need to interpret the different call types. In this work, we present a two-stage classification approach using the labeled call/noise files and a few labeled call-type files. Results indicate a reliable accuracy of 95.0 % for call segmentation and 87 % for classification of 12 call classes. We further visualize the learned orca call representations in the convolutional neural network (CNN) activations to explain the potential of CNN based recognition for bioaccousitc signal
Keywords: orca; bioacousitc signals; CNN; classification; visualization

Segmentation of Cerebral Vasculature - Alicioglu, Yesim; Spiegel, Martin; Wimmer, Andreas; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson’s Disease - Haji Ghassemi, Nooshin; Hannink, Julius; Martindale, Christine; Gaßner, Heiko; Müller, Meinard; Klucken, Jochen; Eskofier, Björn (Sensors, 2018)
Keywords: Parkinson’s Disease; gait analysis; inertial sensors; step segmentation; stride segmentation; accelerometer; gyroscope

Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration - Spiegel, Martin; Hahn, Dieter; Daum, Volker; Wasza, Jakob; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2009)
Active shape models (ASMs) are widely used for applications in the field of image segmentation. Building an ASM requires to determine point correspondences for input training data, which usually results in a set of landmarks distributed according to the statistical variations. State-of-the-art methods solve this problem by minimizing the description length of all landmarks using a parametric mapping of the target shape (e.g. a sphere). In case of models composed of multiple sub-parts or highly non-convex shapes, these techniques feature substantial drawbacks. This article proposes a novel technique for solving the crucial correspondence problem using non-rigid image registration. Unlike existing approaches the new method yields more detailed ASMs and does not require explicit or parametric formulations of the problem. Compared to other methods, the already built ASM can be updated with additional prior knowledge in a very efficient manner. For this work, a training set of 3-D kidney pairs has been manually segmented from 41 CT images of different patients and forms the basis for a clinical evaluation. The novel registration based approach is compared to an already established algorithm that uses a minimum description length (MDL) formulation. The presented results indicate that the use of non-rigid image registration to solve the point correspondence problem leads to improved ASMs and more accurate segmentation results. The sensitivity could be increased by approximately 10%. Experiments to analyze the dependency on the user initialization also show a higher sensitivity of 5-15%. The mean squared error of the segmentation results and the ground truth manually classified data could also be reduced by 20-34% with respect to varying numbers of training samples. © 2008 Elsevier Ltd. All rights reserved.

Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images - Deitsch, Sergiu; Buerhop-Lutz, Claudia; Sovetkin, Evgenii; Steland, Ansgar; Gallwitz, Florian; Maier, Andreas; Rieß, Christian (Machine Vision and Applications, 2021)
High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kind of defects, which is time-consuming and expensive. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. Automated segmentation of cells is a key step in automating the visual inspection workflow. It also enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an F1 score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.<br
Keywords: Photovoltaic modules; Electroluminescence imaging; visual inspection; lens distortion; solar cell extraction

Segmenting into adequate units for automatic recognition of emotion-related episodes: a speech-based approach - Batliner, Anton; Seppi, Dino; Steidl, Stefan; Schuller, Björn (Advances in Human-Computer Interaction, 2010)
We deal with the topic of segmenting emotion-related (emotional/affective) episodes into adequate units for analysis and automatic processing/classification—a topic that has not been addressed adequately so far. We concentrate on speech and illustrate promising approaches by using a database with children’s emotional speech. We argue in favour of the word as basic unit and map sequences of words on both syntactic and “emotionally consistent” chunks and report classification performances for an exhaustive modelling of our data by mapping word-based paralinguistic emotion labels onto three classes representing valence (positive, neutral, negative), and onto a fourth rest (garbage) class.

Self-Calibration and Simultaneous Motion Estimation for C-arm CT using Fiducial Markers - Syben-Leisner, Christopher; Bier, Bastian; Berger, Martin; Aichert, André; Fahrig, Rebecca; Gold, Garry; Levenston, Marc; Maier, Andreas (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen., 2017)
C-arm cone-beam CT systems have an increasing popularity in the clinical environment due to their highly flexible scan trajectories. Recent work used these systems to acquire images of the knee joint under weight-bearing conditions. During the scan, the patient is in a standing or in a squatting position and is likely to show involuntary motion, which corrupts image reconstruction. The state-of-the-art fully automatic motion compensation relies on fiducial markers for motion estimation. Due to the not reproducible horizontal trajectory, the system has to be calibrated with a calibration phantom before or after each scan. In this work we present a method to incorporate a self-calibration into the existing motion compensation framework without the need of prior geometric calibration. Quantitative and qualitative evaluations on a numerical phantom as well as clinical data, show superior results compared to the current state-of-the-art method. Moreover, the clinical workflow is improved, as a dedicated system calibration for weight-bearing acquisitions is no longer required.
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.selfca_1

Self-Encoded Marker Design for Adaptive Optical Real-Time Motion Correction - Forman, Christoph; Aksoy, Murat; Straka, Matus; Hornegger, Joachim; Bammer, Roland (Proceedings of the 18th Annual Meeting of ISMRM & ESMRMB, 2010)

Self-encoded marker for optical prospective head motion correction in MRI - Forman, Christoph; Aksoy, Murat; Hornegger, Joachim; Bammer, Roland (Medical Image Analysis, 2011)
The tracking and compensation of patient motion during a magnetic resonance imaging (MRI) acquisition is an unsolved problem. For brain MRI, a promising approach recently suggested is to track the patient using an in-bore camera and a checkerboard marker attached to the patient's forehead. However, the possible tracking range of the head pose is limited by the fact that the locally attached marker must be entirely visible inside the camera's narrow field of view (FOV). To overcome this shortcoming, we developed a novel self-encoded marker where each feature on the pattern is augmented with a 2-D barcode. Hence, the marker can be tracked even if it is not completely visible in the camera image. Furthermore, it offers considerable advantages over the checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant. The motion correction with the novel self-encoded marker recovered a rotation of 18° around the principal axis of the cylindrical phantom in-between two scans. After rigid registration of the resulting volumes, we measured a maximal error of 0.39 mm and 0.15° in translation and rotation, respectively. In in vivo experiments, the motion compensated images in scans with large motion during data acquisition indicate a correlation of 0.982 compared to a corresponding motion-free reference. © 2011 Elsevier B.V.

Self-encoded Marker for Optical Prospective Head Motion Correction in MRI - Forman, Christoph; Aksoy, Murat; Hornegger, Joachim; Bammer, Roland (Lecture Notes in Computer Science, 2010)

Self-gated MRI motion modeling for respiratory motion compensation in integrated PET/MRI - Grimm, Robert; Fürst, Sebastian; Souvatzoglou, Michael; Forman, Christoph; Hutter, Jana; Dregely, Isabel; Ziegler, Sibylle; Kiefer, Berthold; Hornegger, Joachim; Block, Kai Tobias; Nekolla, Stephan G. (Medical Image Analysis, 2015)
Accurate localization and uptake quantification of lesions in the chest and abdomen using PET imaging is challenged by respiratory motion occurring during the exam. This work describes how a stack-of-stars MRI acquisition on integrated PET/MRI systems can be used to derive a high-resolution motion model, how many respiratory phases need to be differentiated, how much MRI scan time is required, and how the model is employed for motion-corrected PET reconstruction. MRI self-gating is applied to perform respiratory gating of the MRI data and simultaneously acquired PET raw data. After gated PET reconstruction, the MRI motion model is used to fuse the individual gates into a single, motion-compensated volume with high signal-to-noise ratio (SNR). The proposed method is evaluated in vivo for 15 clinical patients. The gating requires 5-7 bins to capture the motion to an average accuracy of 2 mm. With 5 bins, the motion-modeling scan can be shortened to 3-4 min. The motion-compensated reconstructions show significantly higher accuracy in lesion quantification in terms of standardized uptake value (SUV) and different measures of lesion contrast compared to ungated PET reconstruction. Furthermore, unlike gated reconstructions, the motion-compensated reconstruction does not lead to SNR loss.
UnivIS-Import:2015-03-09:Pub.2015.tech.IMMD.IMMD5.selfga
Keywords: Motion compensation; MRI; PET/MRI; Respiratory gating; Respiratory motion

Self-Gated Radial MRI for Respiratory Motion Compensation on Hybrid PET/MR Systems - Grimm, Robert; Fürst, Sebastian; Dregely, Isabel; Forman, Christoph; Hutter, Jana; Ziegler, Sibylle; Nekolla, Stephan G.; Kiefer, Berthold; Schwaiger, Markus; Hornegger, Joachim; Block, Kai Tobias (Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, 2013)

Self-gating Reconstructions of Motion and Perfusion for Free-breathing T1-weighted DCE-MRI of the Thorax Using 3D Stack-of-stars GRE Imaging - Grimm, Robert; Block, Kai Tobias; Hutter, Jana; Forman, Christoph; Hintze, Christian; Kiefer, Berthold; Hornegger, Joachim (Proceedings of International Society for Magnetic Resonance in Medicine, 2012)

Semantic and topological classification of images in magnetically guided capsule endoscopy - Mewes, Philip; Rennert, P.; Juloski, A. Lj.; Lalande, A.; Angelopoulou, Elli; Kuth, R.; Hornegger, Joachim (Medical Imaging 2012: Computer-Aided Diagnosis, 2012)
Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, esophagus, stomach, duodenum). In the third stage two parallel classifications steps distinguish topologically different regions inside the stomach (cardia, fundus, pylorus, antrum, peristaltic view). For image classification, global image features and local texture features were applied and their performance was evaluated. We show that the third classification step can be improved by a bubble and debris segmentation because it limits feature extraction to discriminative areas only. We also investigated the impact of segmenting intestinal folds on the identification of different semantic camera positions. The results of classifications with a support-vector-machine show the significance of color histogram features for the classification of corrupted images (97%). Features extracted from intestinal fold segmentation lead only to a minor improvement (3%) in discriminating different camera positions. © 2012 SPIE.

Semantic Network Meet Bayesian Classifiers - Hornegger, Joachim; Nöth, Elmar; Fischer, Volker; Niemann, Heinrich (Mustererkennung 1996, 1996)

Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation - Vesal, Sulaiman; Diaz-Pinto, Andres; Ravikumar, Nishant; Ellmann, Stephan; Davari, Amirabbas; 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.semiau_5

Semi-automatical Validation of SPECT/CT Scanners - Han, Jingfeng; Bennewitz, Christian; Hornegger, Joachim; Kuwert, Torsten (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Semi-Automatic Basket Catheter Reconstruction from Two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Pattern Recognition, 2015)
Ablation guided by focal impulse and rotor mapping (FIRM) is a novel treatment option for atrial fibrillation, a frequent heart arrhythmia. This procedure is performed minimally invasively and at least partially under fluoroscopic guidance. It involves a basket catheter comprising 64 electrodes. The 3-D position of these electrodes is important during treatment. We propose a novel model-based method for 3-D reconstruction of this catheter using two X-ray images taken from different views. Our approach requires only little user interaction. An evaluation of the method found that the electrodes of the basket catheter can be reconstructed with a median error of 1.5 mm for phantom data and 3.4 mm for clinical data.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.semiau_8

Semi-automatic calibration for dereverberation by spectral subtraction for continuous speech recognition - Riedhammer, Korbinian Thomas; Bocklet, Tobias; Orozco-Arroyave, Juan Rafael; Nöth, Elmar (11th ITG Symposium on Speech Communication, 2014)
In this article, we describe a semi-automatic calibration algorithm for dereverberation by spectral subtraction. We verify the method by a comparison to a manual calibration derived from measured room impulse responses (RIR). We conduct extensive experiments to understand the effect of all involved parameters and to verify values suggested in the literature. The experiments are performed on a text read by 31 speakers and recorded by a headset and three far-field microphones. Results are measured in terms of automatic speech recognition (ASR) performance using a 1-gram model to emphasize acoustic recognition performance. To accommodate for the acoustic change by dereverberation we apply supervised MAP adaptation to the hidden Markov model output probabilities. The combination of dereverberation and adaptation yields a relative improvement of about 35% in terms of word error rate (WER) compared to the original signal.

Semi-Automatic Catheter Model Generation using Biplane X-Ray Images - Käppler, Sebastian; Wu, Wen; Chen, Terrence; Koch, Martin; Kiraly, Attila P.; Strobel, Norbert; Hornegger, Joachim (Proceedings of 2013 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013)

Semi-automatic catheter reconstruction from two views - Hoffmann, Matthias; Brost, Alexander; Jakob, Carolin; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (Lecture Notes in Computer Science, 2012)
We propose novel methods for (a) detection of a catheter in fluoroscopic images and (b) reconstruction of this catheter from two views. The novelty of (a) is a reduced user interaction and a higher accuracy. It requires only a single seed point on the catheter in the fluoroscopic image. Using this starting point, possible parts of the catheter are detected using a graph search. An evaluation of the detection using 66 clinical fluoroscopic images yielded an average error of 0.7 mm +/- 2.0 mm. The novelty of (b) is a better ability to deal with highly curved objects as it selects an optimal set of point correspondences from two point sequences describing the catheters in two fluoroscopic images. The selected correspondences are then used for computation of the 3-D reconstruction. The evaluation on 33 clinical biplane images yielded an average backprojection error of 0.4 mm +/- 0.6 mm.

Semi-Automatic Catheter Reconstruction from Two Views - Hoffmann, Matthias; Brost, Alexander; Jakob, Carolin; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert (MICCAI 2012, Part I, 2012)
We propose novel methods for (a) detection of a catheter in fluoroscopic images and (b) reconstruction of this catheter from two views. The novelty of (a) is a reduced user interaction and a higher accuracy. It requires only a single seed point on the catheter in the fluoroscopic image. Using this starting point, possible parts of the catheter are detected using a graph search. An evaluation of the detection using 66 clinical fluoroscopic images yielded an average error of 0.7 mm +/- 2.0 mm. The novelty of (b) is a better ability to deal with highly curved objects as it selects an optimal set of point correspondences from two point sequences describing the catheters in two fluoroscopic images. The selected correspondences are then used for computation of the 3-D reconstruction. The evaluation on 33 clinical biplane images yielded an average backprojection error of 0.4 mm +/- 0.6 mm.
UnivIS-Import:2015-04-16:Pub.2012.tech.IMMD.IMMD5.semiau_47

Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration - Chen, Shuqing; Gehrer, Simone; Kaliman, Sara; Ravikumar, Nishant; Becit, Abdurrahman; Aliee, Maryam; Dudziak, Diana; Merkel, Rudolf; Smith, Ana-Suncana; Maier, Andreas (Bildverarbeitung für die Medizin 2019. Informatik aktuell., 2019)

Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets - Scherl, Holger; Hornegger, Joachim; Prümmer, Marcus; Lell, Michael (Medical Image Analysis, 2007)
We present a new level-set based method to segment and quantify stenosed internal carotid arteries (ICAs) in 3D contrast-enhanced computed tomography angiography (CTA). Within these data sets it is a difficult task to evaluate the degree of stenoses deterministically even for the experienced physician because the actual vessel lumen is hardly distinguishable from calcified plaque and there is no sharp border between lumen and arterial wall. According to our knowledge no commercially available software package allows the detection of the boundary between lumen and plaque components. Therefore in the clinical environment physicians have to perform the evaluation manually. This approach suffers from both intra- and inter-observer variability. The limitation of the manual approach requires the development of a semi-automatic method that is able to achieve deterministic segmentation results of the internal carotid artery via level-set techniques. With the new method different kinds of plaques were almost completely excluded from the segmented regions. For an objective evaluation we also studied the method's performance with four different phantom data sets for which the ground truth of the degree of stenosis was known a priori. Finally, we applied the method to 10 ICAs and compared the obtained segmentations with manual measurements of three physicians. © 2006 Elsevier B.V. All rights reserved.

Semi-Automatic Manufacturing of Customized Hearing Aids Using a Feature Driven Rule-based Framework - Sickel, Konrad; Baloch, Sajjad; Bubnik, Vojtech; Melkisetoglu, Rupen; Azernikov, Sergei; Fang, Tong; Hornegger, Joachim (Proceedings of the Vision, Modeling, and Visualization Workshop 2009, 2009)

Semi-Permeable Filters for Interior Region of Interest Dose Reduction in X-Ray Microscopy - Huang, Yixing; Mill, Leonid; Stoll, Robert; Kling, Lasse; Aust, Oliver; Wagner, Fabian; Grüneboom, Anika; Schett, Georg; Christiansen, Silke H.; Maier, Andreas (Bildverarbeitung für die Medizin, 2021)
In osteoporosis research, the number and size of lacunae in cortical bone tissue are important characteristics of osteoporosis development. In order to reconstruct lacunae well in X-ray microscopy while protecting bone marrow from high-dose damage in in-vivo experiments, semi-permeable X-ray filters are proposed for dose reduction. Compared with an opaque filter, image quality with a semi-permeable filter is improved remarkably. For image reconstruction, both iterative reconstruction with reweighted total variation (wTV) and FDK reconstruction from penalized weighted least-square (PWLS) processed projections can reconstruct lacunae when the transmission rate of the filter is as small as 5%. However, PWLS is superior in computation efficiency.<br
Keywords: Osteoporosis research; X-ray filter; X-ray microscopy; dose reduction

SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology - Mühlberg, Alexander; Ritter, Paul; Langer, Sabine; Goossens, Chloë; Nübler, Stefanie; Schneidereit, Dominik; Taubmann, Oliver; Denzinger, Felix; Nörenberg, Dominik; Haug, Michael; Schürmann, Sebastian; Horstmeyer, Roarke; et al. (Advanced Science, 2023)
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.
CRIS-Team Scopus Importer:2023-08-25
Keywords: deep learning; explainable artificial intelligence; meta-learning; multiphoton microscopy; muscle research; prior information integration; scientific machine learning

Sensitivity curve approximation using linear algebra - Paulus, Dietrich; Hong, Vinh; Idler, Corvin; Hornegger, Joachim; Csink, Laszlo (CGIV 2004 - Second European Conference on Color in Graphics, Imaging, and Vision and Sixth International Symposium on Multispectral Color Science, 2004)
Calibration of color cameras requires that a calibration pattern is recorded and color responsivity curves are estimated from the recorded data. Very often, these curves are sampled at discrete wave-length, so that the calibration problem can be written as a system of linear equations. The solution of the - usually overdetermined- problem is subject to a constrained optimization problem, as these curves need to have a certain shape. In this article we apply different methods to incorporate constraints into the estimation problem while still keeping the problem linear.

Sensor-based stroke detection and stroke type classification in table tennis - Blank, Peter; Hoßbach, Julian; Schuldhaus, Dominik; Eskofier, Björn (Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.sensor_6
Keywords: Inertial sensors; Movement analysis; Stroke detection and classification; Table tennis

Separate CT-reconstruction for 3D wavelet based noise reduction using correlation analysis - Borsdorf, Anja; Raupach, Rainer; Hornegger, Joachim (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
The projection data measured in computed tomography (CT) and, consequently, the volumes reconstructed from these data contain noise. For a reliable diagnosis and subsequent image processing, like segmentation, the ratio between relevant tissue contrasts and the noise amplitude must be sufficiently large. We propose a novel 3D wavelet based method for structurepreserving noise reduction in CT. By separate reconstructions from disjoint subsets of projections, two volumes can be computed, which only differ with respect to noise. Two disjoint subsets of projections can be directly acquired using a dualsource CT-scanner. Otherwise, the two volumes can be generated by reconstructing even and odd numbered projections separately. Correlation analysis between the approximation coefficients of the two input datasets, combined with an orientation and position dependent noise estimation are used for differentiating between structure and noise at each level of the wavelet decomposition. The proposed method adapts itself to the locally varying noise power and allows an anisotropic denoising. The quantitative and qualitative evaluation on phantom and clinical data showed that noise reduction rates up to 60% can be achieved without noticeable loss of resolution. © 2007 IEEE.

Separate CT-Reconstruction for Orientation and Position Adaptive Wavelet Denoising - Borsdorf, Anja; Raupach, Rainer; Hornegger, Joachim (Bildverarbeitung für die Medizin 2007, 2007)

Shading Correction for Grating-based Differential Phase Contrast X-ray Imaging - Käppler, Sebastian; Wandner, Johannes; Weber, Thomas; Maier, Andreas; Anton, Gisela; Hornegger, Joachim; Riess, Christian (IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2014)

Sharp as a Tack: Measuring and Comparing Edge Sharpness in Motion-Compensated Medical Image Reconstruction - Taubmann, Oliver; Wetzl, Jens; Lauritsch, Günter; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.sharpa_0

Sheet metal forming limits as classification problem - Jaremenko, Christian; Huang, Xiaolin; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Proceedings of the 15th IAPR International Conference on Machine Vision Applications, 2017)
Forming limit diagrams are used to evaluate the formability of metal sheets and describe the maximum strain to failure in terms of major and minor strain. The main idea is the detection of the onset of necking in limited areas of the investigated sheet metals. Current methods introduce location-or time-dependency or may require user interaction. Within this contribution we interpret the onset of necking as classification problem and show that a support vector machine performs comparable to a human experts group. Best results reach up to 87.5 % avg. precision and 84.9 % avg. recall, using all available 2D-information, while being time- and location-independent.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.sheetm

Shift-invariant cone-beam reconstruction outside R-lines with a disconnected source trajectory - Yu, Z.; Noo, F.; Lauritsch, G.; Maier, A.; Dennerlein, F.; Hornegger, Joachim (IEEE Nuclear Science Symposium Conference Record, 2012)
Over the past decade, significant progress has been made in theoretically-exact and stable (TES) cone-beam reconstruction using shift-invariant filtering, provided that Tuy's condition is satisfied. However, these algorithms only allow reconstruction at positions that lie on an R-line (an R-line is any segment of line that connects two source positions). In 2005, Pack and Noo proposed a formula that allows TES reconstruction outside R-lines using shift-invariant filtering. However, for some disconnected source trajectories, the backprojection and filtering steps for this formula are complex. In this work, we propose a new shift-invariant reconstruction formula that allows less complex TES reconstruction outside R-lines for some disconnected source trajectories. This new formula is validated using computer simulated data from a tent trajectory. © 2012 IEEE.
CRIS-Team Scopus Importer:2022-08-05

Signal decomposition for X-ray dark-field imaging - Käppler, Sebastian; Bayer, Florian; Weber, Thomas; Maier, Andreas; Anton, Gisela; Hornegger, Joachim; Beckmann, Matthias; Fasching, Peter; Hartmann, Arndt; Heindl, Felix; Michel, Thilo; Oezguel, Gueluemser; et al. (MICCAI 2014, 2014)
Grating-based X-ray dark-field imaging is a new imaging modality. It allows the visualization of structures at micrometer scale due to small-angle scattering of the X-ray beam. However, reading darkfield images is challenging as absorption and edge-diffraction effects also contribute to the dark-field signal, without adding diagnostic value. In this paper, we present a novel–and to our knowledge the first–algorithm for isolating small-angle scattering in dark-field images, which greatly improves their interpretability. To this end, our algorithm utilizes the information available from the absorption and differential phase images to identify clinically irrelevant contributions to the dark-field image. Experimental results on phantom and ex-vivo breast data promise a greatly enhanced diagnostic value of dark-field image
EVALuna2:6632

Signal Processing for Interventional X-ray-based Coronary Angiography - Unberath, Mathias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)
Rotational angiography using C-arm scanners enables intra-operative 3-D imaging that has proved beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery imaging but remains subject of intensive academic research. 3-D reconstruction of the coronary vasculature is impeded by severe lateral truncation of the thorax, as well as substantial intra-scan respiratory and cardiac motion. Reliable and fully automated solutions to all of the aforementioned problems are required to pave the way for clinical application of rotational angiography and, hence, sustainably change the state-of-care. Within this thesis, we identify shortcomings of existing approaches and devise algorithms that effectively address non-recurrent object motion, severe angular undersampling, and the dependency on projection domain segmentations. The proposed methods build upon virtual digital subtraction angiography (vDSA) that voids image truncation and enables prior-reconstruction-free respiratory motion compensation using both Epipolar consistency conditions (ECC) and auto-focus measures (AFMs). The motion-corrected geometry is then used in conjunction with a novel 4-D iterative algorithm that reconstructs images at multiple cardiac phases simultaneously. The method allows for communication among 3-D volumes by regularizing the temporal total variation (tTV) and thus implicitly addresses the problem of insufficient data very effectively. Finally, we consider symbolic coronary artery reconstruction from very few observations and develop generic extensions that consist of symmetrization, outlier removal, and projection domain-informed topology recovery. When applied to two state-of-the-art reconstruction algorithms, the proposed methods substantially reduce problems due to incorrect 2-D centerlines, promoting improved performance. Given that all methods proved effective on the same in silico and in vivo data sets, we are confident that the proposed algorithms bring rotational coronary angiography one step closer to clinical applicability.<br
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.signal
Keywords: Digital Subtraction Angiography; Respiratory Motion Correction; Tomographic Reconstruction

Simulation künstlicher neuronaler Netze - Wilke, Peter (Arbeitsgemeinschaft Funktionalanalysis, Universität Erlangen-Nürnberg, 1992)

Simulation of Neural Network: ... or the dark side of computational power - Wilke, Peter (University of Western Australia, 1999)

Simulation of Neural Networks in a Distributed Computing Environment using NeuroGraph - Wilke, Peter (Proc. IWANN93 Int. Workshop on Artificial Neural Networks, 1993)

Simulation tools for two-dimensional experiments in x-ray computed tomography using the FORBILD head phantom - Yu, Zhicong; Noo, Frédéric; Dennerlein, Frank; Wunderlich, Adam; Lauritsch, Günter; Hornegger, Joachim (Physics in Medicine and Biology, 2012)
Mathematical phantoms are essential for the development and early stage evaluation of image reconstruction algorithms in x-ray computed tomography (CT). This note offers tools for computer simulations using a two-dimensional (2D) phantom that models the central axial slice through the FORBILD head phantom. Introduced in 1999, in response to a need for a more robust test, the FORBILD head phantom is now seen by many as the gold standard. However, the simple SheppLogan phantom is still heavily used by researchers working on 2D image reconstruction. Universal acceptance of the FORBILD head phantom may have been prevented by its significantly higher complexity: software that allows computer simulations with the SheppLogan phantom is not readily applicable to the FORBILD head phantom. The tools offered here address this problem. They are designed for use with Matlab®, as well as open-source variants, such as FreeMat and Octave, which are all widely used in both academia and industry. To get started, the interested user can simply copy and paste the codes from this PDF document into Matlab® M-files. © 2012 Institute of Physics and Engineering in Medicine.

Simulation und Visualisierung künstlicher neuronaler Netze unter Verwendung paralleler Hardware - Wilke, Peter; Jacob, Christian (1992)
UnivIS-Import:2016-06-23:Pub.1992.tech.IMMD.IMMD2._simul_0

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general.<br
Keywords: X-ray scatter Skin dose Multi-task learning

Simultaneous Generation of X-ray and Range Images using XCAT under Motion - Rybakov, Oleksiy; Bier, Bastian; Maier, Jennifer; Unberath, Mathias; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
Novel algorithms in the field of X-ray imaging are commonly evaluated on simulation software first, before they are implemented on a clinical scanner in order to test their performance in a very controlled setup. This reduces patient dose and facilitates the development of new approaches and methods. In recent years, range imaging applications were established in the field of medical imaging. Range imaging showed its potential in radiotherapy, augmented reality, and, recently, also for motion correction in cone-beam computed tomography. In this work, we present an open-source software tool that allows to generate X-ray projections and surface information in one step completely on the graphics processing unit. To this end, we extend a state-of-the-art rendering method based on an append buffer. Arbitrary imaging geometries for the X-ray source and the range imaging camera can be selected. We test the proposed method on the XCAT phantom in two fundamentally different scenarios: a weight-bearing acquisition showing a squat, and a supine acquisition of a breathing patien
Keywords: range imaging; x-ray imaging; append buffer; weight-bearing; open-source; XCAT

Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair - Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose : Endovascular repair of aortic aneurysms (EVAR) can be supported by fusing pre- and intraoperative data to allow for improved navigation and to reduce the amount of contrast agent needed during the intervention. However, stiff wires and delivery devices can deform the vasculature severely, which reduces the accuracy of the fusion. Knowledge about the 3D position of the inserted instruments can help to transfer these deformations to the preoperative information. Method : We propose a method to simultaneously reconstruct the stiff wires in both iliac arteries based on only a single monoplane acquisition, thereby avoiding interference with the clinical workflow. In the available X-ray projection, the 2D course of the wire is extracted. Then, a virtual second view of each wire orthogonal to the real projection is estimated using the preoperative vessel anatomy from a computed tomography angiography as prior information. Based on the real and virtual 2D wire courses, the wires can then be reconstructed in 3D using epipolar geometry. Results : We achieve a mean modified Hausdorff distance of 4.2 mm between the estimated 3D position and the true wire course for the contralateral side and 4.5 mm for the ipsilateral side. Conclusion : The accuracy and speed of the proposed method allow for use in an intraoperative setting of deformation correction for EVAR.
CRIS-Team Scopus Importer:2019-10-22
Keywords: EVAR; Fluoroscopy; Guide wire reconstruction; Image guidance

Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling - Wetzl, Jens; Schmidt, Michaela; Pontana, François; Longère, Benjamin; Lugauer, Felix; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Magnetic Resonance Materials in Physics Biology and Medicine, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.single_7
Keywords: 3-D CINE imaging; Compressed sensing; Ventricular function

Single-breath-hold abdominal T1 mapping using 3D Cartesian Look-Locker with spatiotemporal sparsity constraints - Lugauer, Felix; Wetzl, Jens; Forman, Christoph; Schneider, Manuel; Kiefer, Berthold; Hornegger, Joachim; Nickel, Dominik; Maier, Andreas (Magnetic Resonance Materials in Physics Biology and Medicine, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.single
Keywords: T1 mapping; 3D Look-Locker; Compressed Sensing; Poisson Sampling

Single Breath-Hold Abdominal T1 Mapping Using 3-D Cartesian Sampling and Spatiotemporally Constrained Reconstruction - Lugauer, Felix; Wetzl, Jens; Forman, Christoph; Schneider, Manuel; Kiefer, Berthold; Nickel, Dominik; Maier, Andreas (Proceedings of the 25th Annual Meeting of the ISMRM (ISMRM 2017), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.single_2
Keywords: Volumetric T1 mapping; Look-Locker; Compressed Sensing

Single Material Beam Hardening Correction in CT Using an Analytical Energy Response Model - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2020)
Material decomposition algorithms for dual energy CT require knowledge on the energy response of the used system. This knowledge can be gained either through calibration measurements or using an explicit analytical model. While the former approach is preferred by vendors, it tends to be intimately linked with the chosen image reconstruction approach and is thus not easily amenable to using other, potentially more advanced, image reconstruction methods. Models for the detector response and the source spectrum have been published in the literature. Here, we investigate how satisfactory these general models are to represent the characteristics of one specific scanner. Specifically, the models are tested by using them to perform a water-based, single material beam hardening correction on real CT data. This test is performed using an anthropomorphic head phantom and the ACR CT accreditation phantom both scanned at two different X-ray tube settings that are typically used for dual energy CT. The scans are performed using a state-of-the-art diagnostic CT scanner (SOMATOM Drive, Siemens Healthcare GmbH, Germany). The images corrected for beam hardening using the models are compared with the classical reconstructions provided by the vendor, for which beam hardening correction is based on real measurements from a calibration phantom. The study demonstrates that the results provided by the vendor can be reproduced with high accuracy (within 1-3 HU in mean value) provided some analytical correction for forward scatter is included as well.<br
Keywords: Beam Hardening; Dual Energy CT; Source Spectrum; Detector Response

SkinNet: A Deep Learning Framework for Skin Lesion Segmentation - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.skinne_5

SlicerOCT: A 3-D visualization platform for orthoplane viewing of OCT(A) datasets - Husvogt, Lennart; Moult, Eric M.; Lee, Byungkun; Waheed, Nadia K.; Hornegger, Joachim; Spaide, Richard F.; Maier, Andreas; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2016)
Purpose : 3D Slicer is a visualization platform for radiographic data (computed tomography, magnetic resonance imaging, ultrasound). The purpose of this study is to extend 3D Slicer to enable simultaneous, orthoplane viewing of volumetric optical coherence tomography (OCT) and OCT angiography (OCTA) data. Methods : We propose an OCT specific module, “SlicerOCT,” which leverages 3D Slicer’s existing capabilities for orthoplane viewing and volume rendering. OCT and OCTA data are stored in different layers, which allows simultaneous display; layers can be toggled on and off. OCT specific functions, such as volume projection and OCTA thresholding, are available. Orthoplane views can be scrolled through in a manner analogous to a conventional radiology viewer and data can be rendered volumetrically. Results : Figure 1 shows a SlicerOCT display of OCT and OCTA data from a patient with exudative age-related macular degeneration (AMD). The choroidal neovascularization (CNV) is clearly visible in the OCT angiograms and corresponding structural en face displays (right panels), while CNV location under the retinal pigment epithelium (RPE) and intraretinal cysts are visible in the OCT B-scan display (bottom left). Figure 2 shows a SlicerOCT display of OCT and OCTA data from a patient with geographic atrophy (GA). Areas of RPE alteration are visible on OCTA, while areas of choriocapillaris loss are seen on OCTA. Unlike traditional en face visualization of OCTA data, orthoplane viewing enables interpretation of individual OCTA B-scans, which can help identify artifacts, such as incorrect segmentation, signal attenuation, and decorrelation tails. Conclusions : SlicerOCT enables simultaneous, orthoplane visualization of OCT and OCTA datasets, which promises to enable more accurate interpretation of OCTA data and is especially crucial for studying diseases whose progression alters both structure and blood flo
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.slicer
Keywords: oct; octa; oct angiography; 3d slicer

SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, 2018)
Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.slider

SLS-Printed E-Band Waveguides and the Impact of Surface Roughness - Hofmann, Andreas; Lomakin, Konstantin; Kleinlein, Micha; Bader, Tobias; Sippel, Mark; Gold, Gerald (European Microwave Conference (EuMC), 2023)

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models - Martindale, Christine; Hönig, Florian Thomas; Strohrmann, Christina; Eskofier, Björn (Sensors, 2017)
Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, ‘in the wild’ data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.
Keywords: hierarchical hidden Markov models; segmentation; smart annotation; cyclic sensor data; semi-supervised learning; annotation cost; activity recognition; gait classification; inertial sensors; wearable sensors

Smart Annotation Tool for Multi-sensor gait based daily activity data - Martindale, Christine; Roth, Nils; Hannink, Julius; Sprager, Sebastijan; Eskofier, Björn (2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2018)
The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson’s patients’ symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, labelled, realistic datasets for training. Most systems used as a ground truth for labelling are restricted to the laboratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the labelling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these labels in a post-processing step. This system enables the collection and labelling of large, fairly realistic datasets where 93% of the automatically generated labels are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environment

Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration - Liebert, Andrzej; Das, Badhan Kumar; Kapsner, Lorenz; Eberle, Jörg; Skwierawska, Dominika; Folle, Lukas; Schreiter, Hannes; Laun, Frederik Bernd; Ohlmeyer, Sabine; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian (European Radiology, 2023)
Objectives: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. Materials and methods: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). Results: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. Conclusion: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. Clinical relevance statement: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. Key Points: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Artifact; Breast; Forecasting; Magnetic resonance imaging; Neural networks (computer)

SmartKom-Public - Horndasch, Axel; Rapp, Horst; Röttger, Hans (SmartKom: Foundations of Multimodal Dialogue Systems, 2006)
The SMARTKOM-Public communications booth is provided with a wide palette of modern communication appliances. SMARTKOM-Public offers a higher degree of privacy, much higher communication bandwidth and optimal quality with regard to input and output results than is available to mobile systems for technical reasons. High rates of data flow and high-resolution screens lead, in contrast to a personal digital assistant (PDA), to increased comfort and better ergonomics. This communication space offers the simultaneous and comfortable use of language, pictorial and data services. A document camera is integrated for the transmission and processing of documents. An interesting further possibility concerns the saving of the user’s data in the communications booth. Upon biometric identification, users are able to access their personal data. The idea of a virtual communications workstation that is capable of traveling with the users and adapting to their needs becomes reality. As the product at hand deals with a communication space for the public, along with supplying services, intuitive usability is of primary importance for acceptance. This is particularly so as even people who do not have access to a home computer with similar services will also make use of SMARTKOM-Public. The human–machine interface is designed to replicate normal human means of communication. This includes the broadest range of free speech dialogue possible and the use of natural gestures. Consideration must be made that conventional uses, and not only those based on multi-modal communication, can be performed from the communication booth. Therefore our project started with a user study. The results are presented in the next section, followed by a description of the implemented functionality and some detailed information on selected realization topics in subsequent section
Keywords: Multi-modal dialog systems; SmartKom; public communication; human–machine interface

SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators - Mattick, Alexander; Mayr, Martin; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation. The generation of realistic-looking handwritten text is important because it can be used for data augmentation in handwritten text recognition (HTR) systems or human-computer interaction. We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods by augmenting the training feedback with a tailored solution to mitigate pen-level artifacts. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system and the separate characters of the word. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Generative adversarial networks; Offline handwriting generation; Patch discriminator

Smartphone Input Using Its Integrated Projector and Built-In Camera - Deitsch, Sergiu; Götzelmann, Timo; Gallwitz, Florian (Human-Computer Interaction. Applications and Services, 2014)
Touch input on modern smartphones can be tedious, especially if the touchscreen is small. Smartphones with integrated projectors can be used to overcome this limitation by projecting the screen contents onto a surface, allowing the user to interact with the projection by means of simple hand gestures. In this work, we propose a novel approach for projector smartphones that allows the user to remotely interact with the smartphone screen via its projection. We detect user's interaction using the built-in camera, and forward detected hand gestures as touch input events to the operating system. In order to avoid costly computations, we additionally use built-in motion sensors. We verify the proposed method using an implementation for the consumer smartphone Samsung Galaxy Beam equipped with a deflection mirror. © 2014 Springer International Publishing.
UnivIS-Import:2017-12-18:Pub.2014.tech.IMMD.IMMD5.smartp
Keywords: Mobile computing; user interfaces; small screen; fat finger problem; touch input; fingertip detection; projector smartphone; DLP projector

Smile Recognition System based on Lip Corners Identification - Royce, Eduard; Setyawan, Iwan; Timotius, Ivanna (The 1st International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE 2014), 2014)
Automatic smile recognition plays an important part in several intelligent image processing systems. This paper presents an automatic smile detection system based on lip corners identification. Two different corner detection algorithms are used in this paper to identify lip corners, i.e., the Harris corner detection and the FAST corner detection. The proposed system is tested using our VISiO smiling face database. Our results show that the Harris corner detector yields the best result with 77.5% accuracy while the FAST corner detector gives 72.5% accuracy. However, these results depend on the method of of determining pavg (the average lip corner position during the training phase).
Keywords: smile recognition; corner detection; lip corner identification.

Social Interaction in Particle Swarm Optimization, the Ranked FIPS, and Adaptive Multi-Swarms - Jordan, Johannes Michael; Helwig, Sabine; Wanka, Rolf (Proceedings of the Genetic and Evolutionary Computation Conference, 2008)

Soft-Computing - Prinzip - Wilke, Peter (1997)

Software-based Performance and Complexity Analysis for the Design of Embedded Classification Systems - Ring, Matthias; Jensen, Ulf; Kugler, Patrick; Eskofier, Björn (Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), 2012)
Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true for example for the fields of sports, automotive and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources. In this paper, we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and at the same time high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification rate, and finally analyzes the complexity of the trained system. To give an impression of the importance of such complexity measures when dealing with limited hardware resources, we present the example analysis of the popular Pima Indians Diabetes data set, where considerable complexity differences between classification systems were revealed.

Software development in medicine technology using medical image processing as an example - Hornegger, Joachim; Reiß, Joachim; Kuwert, Torsten (Computer Science - Research and Development, 2008)
In this paper we give a broad overview of modalities used in modern medical imaging and image processing. The whole image processing pipeline including image acquisition, image pre- and post-processing, reconstruction and multi-modal image registration is introduced. We roughly describe the user requirements of medical image processing algorithms and their implication to the system architecture and the software-engineering process as it is established in medical engineering. © 2008 Springer-Verlag.

Software Engineering for Image Processing and Analysis - Paulus, Dietrich; Hornegger, Joachim; Niemann, Heinrich (unbekannt, 1999)

Softwareentwicklung in der Medizintechnik am Beispiel der medizinischen Bildverarbeitung - Hornegger, Joachim; Reiß, Joachim; Kuwert, Torsten (Informatik - Forschung und Entwicklung, 2008)

Software für RISC, optimierende Compiler - Wilke, Peter (RISC-Architekturen, Reihe Informatik, vol. 60, 1988)

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

Somnography using unobtrusive motion sensors and Android-based mobile phones - Gradl, Stefan; Leutheuser, Heike; Kugler, Patrick; Biermann, Teresa; Kreil, Sebastian; Kornhuber, Johannes; Bergner, Matthias; Eskofier, Björn (IEEE EMBC 2013, 2013)
Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50%of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. © 2013 IEEE.

Sparse Active Triangulation Grids for Respiratory Motion Management - Bauer, Sebastian; Ettl, Svenja; Wasza, Jakob; Willomitzer, Florian; Huber, Franz; Hornegger, Joachim; Häusler, Gerd (DGaO Proceedings 2012, 2012)

Sparse Depth Sampling for Interventional 2-D/3-D Overlay: Theoretical Error Analysis and Enhanced Motion Estimation - Wang, Jian; Riess, Christian; Borsdorf, Anja; Heigl, Benno; Hornegger, Joachim (Proceedings of the 15th International Conference on Computer Analysis of Images and Patterns - Part I, 2013)

Sparse Depth Sampling for Interventional 2-D/3-D Overlay: Theoretical Error Analysis and Enhanced Motion Estimation - Wang, Jian; Riess, Christian; Borsdorf, Anja; Heigl, Benno; Hornegger, Joachim (Computer Analysis of Images and Patterns, 2013)

Sparse Principal Axes Statistical Surface Deformation Models for Respiration Analysis and Classication - Wasza, Jakob; Bauer, Sebastian; Haase, Sven; Hornegger, Joachim (Bildverarbeitung für die Medizin 2012, 2012)

Sparsity Level Constrained Compressed Sensing for CT Reconstruction - Wu, Haibo; Hornegger, Joachim (Bildverarbeitung für die Medizin 2012, 2012)

Spatial orientation in translumenal surgery - Höller, Kurt Emmerich; Schneider, Armin; Jahn, Jasper; Gutierrez, Javier; Wittenberg, Thomas; Feussner, Hubertus; Hornegger, Joachim (Minimally Invasive Therapy & Allied Technologies, 2010)
"Natural Orifice Translumenal Endoscopic Surgery" (NOTES) is assumed to offer significant benefits to patients, such as reduced trauma as well as reduced collateral damage. But the potential advantages of this new technology can only be achieved through safe and standardized operation methods. Several barriers, which have been identified during clinical practice in flexible intra-abdominal endoscopy, can only be solved with computer-assisted surgical (CAS) systems. In order to assist the surgeon during the intervention and enhance his visual possibilities, some of these CAS systems require 3-D information of the intervention site, for others 3-D information is even mandatory. Therefore it is evident that the definition and design of new technologies for CAS systems must be strongly considered. A 3-D endoscope, called "Multisensor-Time-of-Flight" (MUSTOF) endoscope, is actually being developed. Within these developments, an optical 3-D time-of-flight (TOF) sensor is attached to the proximal end of a common endoscope. The 3-D depth information obtained by this enhanced endoscope can furthermore be registered with preoperatively acquired 3-D volumetric datasets such as CT or MRI. These enhanced or augmented 3-D data volumes could then be used to find the transgastric or transcolonic entry point to the abdomen. Furthermore, such acquired endoscopic depth data can be used to provide better orientation within the abdomen. Moreover it can also prevent intra-operative collisions and provide an optimized field of view with the possibility for off-axis viewing. Furthermore, providing a stable horizon on video-endoscopic images, especially within non-rigid endoscopic surgery scenarios (particularly within NOTES), remains an open issue. Hence, our recently presented "endorientation" approach for automated image orientation rectification could turn out as an important contribution. It works with a tiny micro-electro-mechanical systems (MEMS) tri-axial inertial sensor that is placed on the distal tip of an endoscope. By measuring the impact of gravity on each of the three orthogonal axes the rotation angle can be estimated with some calculations out of these three acceleration values, which can be used to automatically rectify the endoscopic images using image processing methods. Using such enhanced, progressive endoscopic system extensions proposed in this article, translumenal surgery could in the future be performed in a safer and more feasible manner. © 2010 Informa Healthcare.

Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction - Wu, Haibo; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Medical Imaging 2012: Physics of Medical Imaging, 2012)
Four dimensional computed tomography (4D-CT) is very important for treatment planning in thorax or abdomen area, e.g. for guiding radiation therapy planning. The respiratory motion makes the reconstruction problem illposed. Recently, compressed sensing theory was introduced. It uses sparsity as a prior to solve the problem and improves image quality considerably. However, the images at each phase are reconstructed individually. The correlations between neighboring phases are not considered in the reconstruction process. In this paper, we propose the spatial-temporal total variation regularization (STTVR) method which not only employs the sparsity in the spatial domain but also in the temporal domain. The algorithm is validated with XCAT thorax phantom. The Euclidean norm of the reconstructed image and ground truth is calculated for evaluation. The results indicate that our method improves the reconstruction quality by more than 50% compared to standard ART. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Spatio–Temporal Digital Video Hashing using Edge Orientation Histogram and Discrete Cosine Transform - Setyawan, Iwan; Timotius, Ivanna (International Conference on Information Technology Systems and Innovation, 2014)
In this paper we propose a spatio-temporal digital video hashing scheme. In the proposed scheme, the digital video is treated as a three dimensional signal. Edge Orientation Histogram (EOH) is computed for each frame in the digital video, then a temporal discrete cosine transform (DCT) for the magnitude of each EOH bin is computed. A subset of the resulting DCT coefficients are then pseudo-randomly rearranged to construct the hash. Our experiments show that the proposed system has good discriminating power, is robust against content-preserving attacks but is sensitive to content-altering attacks.
Keywords: digital video hashing; digital video authentication; edge orientation histogram; discrete cosine transform.

Spatio-Temporal Handwriting Imitation - Mayr, Martin; Stumpf, Martin; Nicolaou, Anguelos; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (European Conference on Computer Vision, 2020)
Most people think that their handwriting is unique and cannot be imitated by machines, especially not using completely new content. Current cursive handwriting synthesis is visually limited or needs user interaction. We show that subdividing the process into smaller subtasks makes it possible to imitate someone’s handwriting with a high chance to be visually indistinguishable for humans. Therefore, a given handwritten sample will be used as the target style. This sample is transferred to an online sequence. Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence. This new text is rendered and style-adapted to the input pen. We show the effectiveness of the pipeline by generating in- and out-of- vocabulary handwritten samples that are validated in a comprehensive user study. Additionally, we show that also a typical writer identification system can partially be fooled by the created fake handwritings.<br
Keywords: Offline Handwriting Generation; Style Transfer; Forgery; Handwriting Synthesis

Spatio-temporally Regularized 4-D Cardiovascular C-arm CT Reconstruction Using a Proximal Algorithm - Taubmann, Oliver; Unberath, Mathias; Lauritsch, Günter; Achenbach, Stephan; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 2017)
Tomographic reconstruction of cardiovascular structures from rotational angiograms acquired with interventional C-arm devices is challenging due to cardiac motion. Gating strategies are widely used to reduce data inconsistency but come at the cost of angular undersampling. We employ a spatio-temporally regularized 4-D reconstruction model, which is solved using a proximal algorithm, to handle the substantial undersampling associated with a strict gating setup. In a numerical phantom study based on the CAVAREV framework, similarity to the ground truth is improved from 82.3% to 87.6%by this approach compared to a state-of-the-art motion compensation algorithm, whereas previous regularized methods evaluated on this phantom achieved results below 80%. We also show first image results for a clinical patient data set.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.spatio_6
Keywords: C-arm CT; Cardiovascular reconstruction; Spatio-temporal regularization; Total variation

Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification - Vesal, Sulaiman; Gu, Mingxuan; Maier, Andreas; Ravikumar, Nishant (IEEE Journal of Biomedical and Health Informatics, 2020)
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm2, 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences
Keywords: Left Ventricle Quantification; Cardiac MRI; Cardiac Segmentation; Deep Learning; Myocardial Infraction

Speaker models for monitoring Parkinson’s disease progression considering different communication channels and acoustic conditions - Arias Vergara, Tomás; Vasquez Correa, Juan; Rafael Orozco-Arroyave, Juan; Nöth, Elmar (Speech Communication, 2018)

Speaker model to monitor the neurological state and the dysarthria level of patients with parkinson’s disease - Vasquez Correa, Juan; Castrillon, Reinel; Arias Vergara, Tomás; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Springer Verlag, 2017)
Keywords: Speaker model; UPDRS; Articulation; Parkinson’s disease; Dysarthria; Prosody; Phonation

Speaker verification system for online education platforms - Garcia-Ospina, Nicanor; Orozco Arroyave, Juan Rafael; Vargas-Bonilla, Jesus-Francisco (2018 52ND ANNUAL IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST), 2018)
CRIS-Team WoS Importer:2019-03-12

Speaking Clearly, Understanding Better: Predicting the L2 Narrative Comprehension of Chinese Bilingual Kindergarten Children Based on Speech Intelligibility Using a Machine Learning Approach - Hung, Hiuching; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Maier, Andreas; Nöth, Elmar (Interspeech 2023, 2023)
This study investigated the relationship of speech intelligibility and the narrative comprehension among bilingual kindergarten children and how well the speech intelligibility of second language (L2) predicted the L2 narrative comprehension using a machine learning approach. Fifty Chinese-English bilingual children aged 5-6 years old participated in this study by taking a narrative comprehension test. Their L2 narrative comprehension was assessed using the MAIN test. The speech intelligibility was assessed in terms of twenty-four features that encode confidence levels with respect to phoneme and word classifiers trained on native speaker speech data. Our hypothesis posits that it is possible to predict L2 narrative comprehension based on speech intelligibility features. By using seven out of the twenty-four considered features we were able to make predictions of the MAIN test scores with an RMSE of 2.13 and a Pearson correlation coefficient of 0.468 based on a data set of 50 bilingual kindergarten children. We conclude the paper by providing pedagogical implications for second language teaching as well as suggestions for future work.<br
CRIS-Team Scopus Importer:2023-09-29
Keywords: bilingual kindergarten children; computational paralinguistics; human-computer interaction; L2 narrative comprehension; speech intelligibility; speech recognition

Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words - Orozco-Arroyave, Juan Rafael; Hönig, Florian Thomas; Arias-Londoño, Julián David; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar (Expert Systems, 2015)
UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.spectr

Spectrum Binning Approach for Multi-Material Beam Hardening Correction (MMBHC) in CT - Yang, Qiao; Wu, Meng; Maass, Nicole; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (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.spectr_5

Speech-based Non-prototypical Affect Recognition for Child-Robot Interaction in Reverberated Environments - Wöllmer, Martin; Weninger, Felix; Steidl, Stefan; Batliner, Anton; Schuller, Björn (Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), 2011)
We present a study on the effect of reverberation on acoustic-linguistic recognition of non-prototypical emotions during child-robot interaction. Investigating the well-defined Interspeech 2009 Emotion Challenge task of recognizing negative emotions in children’s speech, we focus on the impact of artificial and real reverberation conditions on the quality of linguistic features and on emotion recognition accuracy. To maintain acceptable recognition performance of both, spoken content and affective state, we consider matched and multi-condition training and apply our novel multi-stream automatic speech recognition system which outperforms conventional Hidden Markov Modeling. Depending on the acoustic condition, we obtain unweighted emotion recognition accuracies of between 65.4% and 70.3% applying our multi-stream system in combination with the SimpleLogistic algorithm for joint acoustic-linguistic analysis.
Keywords: child-robot interaction; affective computing; acoustic-linguistic emotion recognition; reverberation

Speech Classification for Sigmatism in Children - Botinhao, Cassia; Nöth, Elmar; Hornegger, Joachim; Maier, Andreas (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Speech differences between CI users with pre- and postlingual onset of deafness detected by speech processing methods on voiceless to voice transitions - Arias Vergara, Tomás; Orozco Arroyave, Juan Rafael; Vasquez Correa, Juan; Nöth, Elmar; Schuster, Maria; Gollwitzer, Sandra; Högerle, Catalina (Laryngo-Rhino-Otol 2019, 2019)
Introduction: The onset of deafness affects speech in different ways. Speech differences of Cochlear Implant (CI) users with pre- and postlingual deafness are examined using acoustic features extracted automatically from speech. Methods: Utterances of 22 prelingual (15 up to 71 years old) and 22 postlingual CI users (15 up to 78 years old) were analyzed. All patients read 97 words, which contain every phoneme of the German language in different positions within the words. Speech analysis is performed in the transitions from voiceless to voiced sounds that mark the precise control of speech movement patterns. To extract the transitions, we search for the boundary between voiceless and voiced sounds using the fundamental frequency with a constant segment of 80 ms to the left and right. The feature set includes 13 Mel-Frequency Cepstral Coefficients and their 1st and 2nd derivatives. The mean, standard deviation, skewness and kurtosis are computed from the descriptors, forming a 156-dimensional feature vector. Wilcoxon signed-rank test is used to find differences between the pre- and postlingual groups. Results: The Wilcoxon signed-rank test was performed for each descriptor and significant differences between the pre- and postlingual groups (α < 0.05) were found in 8 of the 156 features. Additionally, a support vector regressor was trained to evaluate the age independence of the selected features. According to the results, there was not a strong correlation between the age of the speakers and the selected features (ρ < 0.40). Conclusions: Speech patterns differ significantly between pre- and postlingual CI users at the transitions of voiceless to voiced sounds. Other acoustic features are to be examined and considered in the rehabilitation after cochlear implantation.
Keywords: Speech processing; Automatic acoustic analysis; Cochlear Implants; Voice/voiceless sounds

Speech intelligibility enhancement after maxillary denture treatment and its impact on quality of life - Knipfer, Christian; Riemann, Max; Bocklet, Tobias; Nöth, Elmar; Schuster, Maria; Sokol, Biljana; Eitner, Stephan; Nkenke, Emeka; Stelzle, Florian (International Journal of Prosthodontics, 2014)
Tooth loss and its prosthetic rehabilitation significantly affect speech intelligibility. However, little is known about the influence of speech deficiencies on oral health-related quality of life (OHRQoL). The aim of this study was to investigate whether speech intelligibility enhancement through prosthetic rehabilitation significantly influences OHRQoL in patients wearing complete maxillary dentures. Speech intelligibility by means of an automatic speech recognition system (ASR) was prospectively evaluated and compared with subjectively assessed Oral Health Impact Profile (OHIP) scores.Speech was recorded in 28 edentulous patients 1 week prior to the fabrication of new complete maxillary dentures and 6 months thereafter. Speech intelligibility was computed based on the word accuracy (WA) by means of an ASR and compared with a matched control group. One week before and 6 months after rehabilitation, patients assessed themselves for OHRQoL.Speech intelligibility improved significantly after 6 months. Subjects reported a significantly higher OHRQoL after maxillary rehabilitation with complete dentures. No significant correlation was found between the OHIP sum score or its subscales to the WA.Speech intelligibility enhancement achieved through the fabrication of new complete maxillary dentures might not be in the forefront of the patients' perception of their quality of life. For the improvement of OHRQoL in patients wearing complete maxillary dentures, food intake and mastication as well as freedom from pain play a more prominent role.
EVALuna2:24365

Speech recognition with μ-law companded features on reverberated signals - Haderlein, Tino; Stemmer, Georg; Nöth, Elmar; Haderlein, Tino (Proceedings on the 6th International Conference on Text, Speech, Dialogue - TSD 2003, 2003)
One of the goals of the EMBASSI project is the creation of a speech interface between a user and a TV set or VCR. The interface should allow spontaneous speech recorded by microphones far away from the speaker. This paper describes experiments evaluating the robustness of a speech recognizer against reverberation. For this purpose a speech corpus was recorded with several different distortion types under real-life conditions. On these data the recognition results for reverberated signals using μ-law companded features were compared to an MFCC baseline system. Trained with clear speech, the word accuracy for the μ-law features on highly reverberated signals was 3 percent points better than the baseline result.

Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders - Khun Jush, Farnaz; Dueppenbecker, Peter M.; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
Recent studies showed the possibility of extracting SoS information from pulse-echo ultrasound raw data (a.k.a. RF data) using deep neural networks that are fully trained on simulated data. These methods take sensor domain data, i.e., RF data, as input and train a network in an end-to-end fashion to learn the implicit mapping between the RF data domain and the SoS domain. However, such networks are prone to overfitting to simulated data which results in poor performance and instability when tested on measured data. We propose a novel method for SoS mapping employing learned representations from two linked autoencoders. We test our approach on simulated and measured data acquired from human breast mimicking phantoms. We show that SoS mapping is possible using the learned representations by linked autoencoders. The proposed method has a Mean Absolute Percentage Error (MAPE) of 2.39 % on the simulated data. On the measured data, the predictions of the proposed method are close to the expected values (MAPE of 1.1 % ). Compared to an end-to-end trained network, the proposed method shows higher stability and reproducibility.
CRIS-Team Scopus Importer:2023-12-15
Keywords: Convolutional Autoencoder; Representation Learning; Speed-of-sound Mapping

Split-spectrum amplitude-decorrelation angiography with optical coherence tomography - Jia, Yali; Tan, Ou; Tokayer, Jason; Potsaid, Benjamin; Wang, Yimin; Liu, Jonathan J.; Kraus, Martin; Subhash, Hrebesh; Fujimoto, James G.; Hornegger, Joachim; Huang, David (Optics Express, 2012)
Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction. To overcome this limitation, we developed split-spectrum amplitude-decorrelation angiography (SSADA) to improve the signal-to-noise ratio (SNR) of flow detection. The full OCT spectrum was split into several narrower bands. Inter-B-scan decorrelation was computed using the spectral bands separately and then averaged. The SSADA algorithm was tested on in vivo images of the human macula and optic nerve head. It significantly improved both SNR for flow detection and connectivity of microvascular network when compared to other amplitude-decorrelation algorithms. © 2012 Optical Society of America.

Spracherkennung - Stand der Technik, Einsatzmöglichkeiten und Perspektiven - Gallwitz, Florian; Niemann, Heinrich; Nöth, Elmar (Wirtschaftsinformatik, 1999)

Sprach- und Bilderkennung - Görz, Günther; Hornegger, Joachim (Taschenbuch der Medieninformatik, 2005)

Sprachverständlichkeit und Krankheitsverarbeitung nach der Therapie von Mundhöhlenkarzinomen - Sambale, Maria; Schuster, Maria; Bocklet, Tobias; Maier, Andreas; Eysholdt, Ulrich; Ströbele, Anika; Stelzle, Florian (Laryngo-Rhino-Otologie, 2010)

Sprechen: Ein Hindernis in der modernen Mensch-Maschine-Kommunikation? - Frank, Carmen Manuela; Nöth, Elmar (Fortschritte der Akustik - Proc. DAGA 05, 2005)

SQEL: A Multilingual and Multifunctional Dialogue System - Aretoulaki, Maria; Harbeck, Stefan; Gallwitz, Florian; Nöth, Elmar; Niemann, Heinrich; Ivanecky, Josef; Ipsic, Ivo; Pavesic, Nikola; Matoušek, Václav (Proc. Int. Conf. on Spoken Language Processing, 1998)

SR²: Super-Resolution With Structure-Aware Reconstruction - Schirrmacher, Franziska; Lorch, Benedikt; Stimpel, Bernhard; Köhler, Thomas; Riess, Christian (2020 IEEE International Conference on Image Processing (ICIP), 2020)
Image reconstruction is particularly difficult when the type of image degradations are unknown. This may be the case if the acquisition device is unknown or the images stem from an uncontrolled environment like the internet. Yet, it may be important to reconstruct a specific piece of information from the image, such as digits from signs or vehicle license plates. Existing works incorporate such prior information with a sequential super-resolution and classification pipeline. However, this approach is prone to error propagation. In this work, we propose a new approach of connecting classification and super-resolution in parallel within a multi-task network. We show that this architecture is able to preserve structures and to remove noisy pixels although the network itself has never been trained on noisy data. We also show that this design allows to transparently trade classification and super-resolution quality. On upsampling by factor 4, we outperform sequential approaches in terms of SSIM by 10% and improve classification by 69
Keywords: Deep learning; Multi-task learning; Super-resolution; Classification

Standardization of intensity-values acquired by Time-of-Flight-cameras - Stürmer, Michael; Penne, Jochen; Hornegger, Joachim (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, 2008)
The intensity-images captured by Time-of-Flight (ToF)-cameras are biased in several ways. The values differ significantly, depending on the integration time set within the camera and on the distance of the scene. Whereas the integration lime leads to an almost linear scaling of the whole image, the attenuation due to the distance is nonlinear, resulting in higher intensities for objects closer to the camera. The background regions that are farther away contain comparably low values, leading to a bad contrast within the image. Another effect is that some kind of specularity may be observed due to uncommon reflecting conditions at some points within the scene. These three effects lead to intensity images which exhibit significantly different values depending on the integration time of the camera and the distance to the scene, thus making parameterization of processing steps like edge-detection, segmentation, registration and threshold computation a tedious task. Additionally, outliers with exceptionally high values lead to insufficient visualization results and problems in processing. In this work we propose scaling techniques which generate images whose intensities are independent of the integration time of the camera and the measured distance. Furthermore, a simple approach for reducing specularity effects is introduced. © 2008 IEEE.

Statistical classifiers in computer vision - Hornegger, Joachim; Paulus, Dietrich; Niemann, Heinrich (Data Highways and Information Flooding, a Challenge for Classification and Data Analysis, 1997)
This paper introduces a unified Bayesian approach to 3–D computer vision using segmented image features. The theoretical part summarizes the basic requirements of statistical object recognition systems. Non–standard types of models are introduced using parametric probability density functions, which allow the implementation of Bayesian classifiers for object recognition purposes. The importance of model densities is demonstrated by concrete examples. Normally distributed features are used for automatic learning, localization, and classification. The contribution concludes with the experimental evaluation of the presented theoretical approach.

Statistical classifiers in computer vision - Hornegger, Joachim; Paulus, Dietrich; Niemann, Heinrich (Classification, Data Analysis, and Data Highways, 1998)

Statistical learning, localization, and identification of objects - Hornegger, Joachim; Niemann, Heinrich (Proceedings of the 5th International Conference on Computer Vision, 1995)
This work describes a statistical approach to deal with learning and recognition problems in the field of computer vision. An abstract theoretical framework is provided, which is suitable for automatic model generation from examples, identification, and localization of objects. Both, the learning and localization stage are formalized as parameter estimation tasks. The statistical learning phase is unsupervised with respect to the matching of model and scene features. The general mathematical description yields algorithms which can even treat parameter estimation problems from projected data. The experiments show that this probabilistic approach is suitable for solving 2D and 3D object recognition problems using grey-level images. The method can also be applied to 3D image processing issues using range images, i.e. 3D input data.

Statistical Modeling of Relations for 3-D Object Recognition - Hornegger, Joachim (Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1997)

Statistische Modellierung, Klassifikation und Lokalisation von Objekten - Hornegger, Joachim (Shaker, 1996)

Statistische Modellierung, Klassifikation und Lokalisation von Objekten - Hornegger, Joachim (Die besten Informatik-Dissertationen 1996, 1997)
UnivIS-Import:2015-04-20:Pub.1997.tech.IMMD.IMMD5.statis_6

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

Stimmqualitätsmessung mittels prosodischer Analyse verschiedener gelesener Textabschnitte - Haderlein, Tino; Nöth, Elmar; Döllinger, Michael; Schützenberger, Anne (Aktuelle phoniatrisch-pädaudiologische Aspekte 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.stimmq_9
Keywords: Sprache

Streak Artifact Reduction in Limited Angle Tomography Using Machine Learning - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.streak

Stride Segmentation During Free Walk Movements Using Multi-dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data - Barth, Jens; Oberndorfer, Cäcilia; Pasluosta, Cristian Federico; Schülein, Samuel; Gaßner, Heiko; Reinfelder, Samuel; Kugler, Patrick; Schuldhaus, Dominik; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Sensors, 2015)
UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.stride
Keywords: inertial sensors; stride segmentation; accelerometer; gyroscope; dynamic time warping; free walk; gait analysis; Parkinsons disease; geriatric patients; movement impairments

Structure-Enhancing Visualization for Manual Registration in Fluoroscopy - Hoffmann, Matthias; Bourier, Felix; Strobel, Norbert; Hornegger, Joachim (Bildverarbeitung für die Medizin 2013, 2013)

STXM goes 3D: Digital reconstruction of focal stacks as novel approach towards confocal soft x-ray microscopy - Späth, Andreas; Schöll, Simon; Riess, Christian; Schmidtel, Daniel; Paradossi, Gaio; Raabe, Jörg; Hornegger, Joachim; Fink, Rainer (Ultramicroscopy, 2014)
Fresnel zone plate based soft x-ray transmission microspectroscopy has developed into a routine technique for high-resolution elemental or chemical 2D imaging of thin film specimens. The availability of high resolution Fresnel lenses with short depth of focus offers the possibility of optical slicing (in the third dimension) by focus series with resolutions in the submicron regime. We introduce a 3D reconstruction algorithm that uses a variance-based metric to assign a focus measure as basis for volume rendering. The algorithm is applied to simulated geometries and opaque soft matter specimens thus enabling 3D visualization. These studies with z-resolution of few 100. nm serve as important step towards the vision of a confocal transmission x-ray microscope. © 2014 Elsevier B.

Superior Reproducibility of Automated 3-D Surgical Anatomy of Normal and Abnormal Mitral Valve when Compared to a Manual Approach - Voigt, Ingmar; et al. (American College of Cardiology Annual Scientific Session & Expo, 2011)

Super-Resolved Retinal Image Mosaicing - Köhler, Thomas; Heinrich, Axel; Maier, Andreas; Hornegger, Joachim; Tornow, Ralf-Peter (2016 IEEE 13th International Symposium on Biomedical Imaging, 2016)
The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution images captured with non-mydriatic, mobile and video-capable but low-cost cameras. Within the scope of one examination, we scan different regions on the retina by exploiting eye motion conducted by a patient guidance. Appropriate views for our mosaicing method are selected based on optic disk tracking to trace eye movements. For each view, one super-resolved image is reconstructed by fusion of multiple video frames. Finally, all super-resolved views are registered to a common reference using a novel polynomial registration scheme and combined by means of image mosaicing. We evaluated our framework for a mobile and low-cost video fundus camera. In our experiments, we reconstructed retinal images of up to 30° FOV from 10 complementary views of 15° FOV. An evaluation of the mosaics by human experts as well as a quantitative comparison to conventional color fundus images encourage the clinical usability of our framework.
UnivIS-Import:2017-11-07:Pub.2016.tech.IMMD.IMMD5.superr_1
Keywords: eye tracking; fundus video imaging; mosaicing; Retinal imaging; super-resolution

Suppression of shock based errors with gravity related endoscopic image rectification - Höller, Kurt Emmerich; Penne, Jochen; Schneider, Armin; Jahn, Jasper; Girgis, Hani; Guttierrez, Javier; Wittenberg, Thomas; Feußner, Hubertus; Hornegger, Joachim (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Surface-based Respiratory Motion Classification and Verification - Müller, Kerstin; Schaller, Christian; Penne, Jochen; Hornegger, Joachim (Bildverarbeitung für die Medizin 2009, 2009)

Surface Registration to Estimate Motion in CBCT - Bier, Bastian; Unberath, Mathias; Ravikumar, Nishant; Maier, Jennifer; Gooya, Ali; Taylor, Zeike A.; Frangi, Alejandro F.; 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.surfac

Surface segmentation and classification of 3d shapes using dynamic programming - Hornegger, Joachim; Paulus, Dietrich (Pattern Recognition and Image Analysis, 1993)

Surgical mask detection with deep recurrent phonetic models - Klumpp, Philipp; Arias Vergara, Tomás; Vásquez-Correa, Juan Camilo; Pérez-Toro, Paula Andrea; Hönig, Florian Thomas; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (Interspeech 2020, 2020)
To solve the task of surgical mask detection from audio recordings in the scope of Interspeech’s ComParE challenge, we introduce a phonetic recognizer which is able to differentiate between clear and mask samples. A deep recurrent phoneme recognition model is first trained on spectrograms from a German corpus to learn the spectral properties of different speech sounds. Under the assumption that each phoneme sounds differently among clear and mask speech, the model is then used to compute frame-wise phonetic labels for the challenge data, including information about the presence of a surgical mask. These labels served to train a second phoneme recognition model which is finally able to differentiate between mask and clear phoneme productions. For a single utterance, we can compute a functional representation and learn a random forest classifier to detect whether a speech sample was produced with or without a mask. Our method performed better than the baseline methods on both validation and test set. Furthermore, we could show how wearing a mask influences the speech signal. Certain phoneme groups were clearly affected by the obstruction in front of the vocal tract, while others remained almost unaffected.<br
Keywords: computational paralinguistics; phoneme recognition

Surgical mask detection with deep recurrent phonetic models - Klumpp, Philipp; Arias Vergara, Tomás; Vasquez Correa, Juan; Pérez Toro, Paula Andrea; Hönig, Florian Thomas; Nöth, Elmar; Orozco-Arroyave, Juan-Rafael (21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, 2020)
Keywords: Phoneme recognition; Computational paralinguistics

Surrogate-Driven Estimation of Respiratory Motion and Layers in X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.surrog

Swallowing disorders analysis using surface EMG biomarkers and classification models - Roldan-Vasco, Sebastian; Orozco-Duque, Andres; Orozco Arroyave, Juan Rafael (Digital Signal Processing, 2023)
The swallowing process involves complex muscle coordination mechanisms, whose alterations are known as dysphagia. Its instrumental diagnosis is performed by invasive and expensive methods. The surface electromyography (sEMG) emerges as an alternative for automated and objective evaluations of dysphagia symptoms. In this paper we consider thirty-one healthy and 29 dysphagic patients who performed swallowing tasks with water, yogurt, saliva and crackers. The sEMG activity was recorded using bilateral channels for masseter, suprahyoid and infrahyoid muscle groups. Two main analyses were performed. Features in time, frequency, time-frequency, and nonlinear dynamics domains were analyzed to find biomarkers suitable to model dysphagia. Additionally, the automatic discrimination of dysphagia was evaluated with three classification scenarios using: (1) individual features, (2) individual muscle groups, and (3) the combination of muscle groups. Time-features domain exhibited a well-defined representation pattern of swallowing, and achieved the highest individual classification performance (AUC>0.8). The two scenarios with muscle groups yielded the best results along the experiments (AUC>0.85). The best classification results are found with the suprahyoid and masseter muscles, in water and saliva intake. As the main result of the study, we proposed a set of sEMG related biomarkers and classification approaches suitable for automatic dysphagia screening, a step forward in the implementation of non-invasive and objective strategies for swallowing evaluation.
CRIS-Team Scopus Importer:2022-12-02
Keywords: Dysphagia; Feature selection; Machine learning; sEMG; Signal processing

Swept source optical coherence microscopy using a 1310 nm VCSEL light source - Ahsen, Osman; Tao, Yuankai Kenny; Potsaid, Benjamin; Sheikine, Yuri; Jiang, James; Grulkowski, Ireneusz; Tsai, Tsung-Han; Jayaraman, Vijaysekhar; Kraus, Martin; Connolly, James L.; Hornegger, Joachim; Cable, Alex E.; et al. (Optics Express, 2013)

Symmetry, Outliers, and Geodesics in Coronary Artery Centerline Reconstruction from Rotational Angiography - Unberath, Mathias; Taubmann, Oliver; Hell, Michaela; Achenbach, Stephan; Maier, Andreas (Medical Physics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.symmet
Keywords: angiography; cone-beam CT; postprocessing; spanning trees

Symmetry prior for epipolar consistency - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Kowarschik, Markus; Hoppe, Elisabeth; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: For a perfectly plane symmetric object, we can find two views—mirrored at the plane of symmetry—that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we automatically have a second, virtual image of that object if the 3-D location of the symmetry plane is known. Methods: We propose a method for estimating the symmetry plane from a set of projection images as the solution of a consistency maximization based on epipolar consistency. With the known symmetry plane, we can exploit symmetry to estimate in-plane motion by introducing the X-trajectory that can be acquired with a conventional short-scan trajectory by simply tilting the acquisition plane relative to the plane of symmetry. Results: We inspect the symmetry plane estimation on a real scan of an anthropomorphic human head phantom and show the robustness using a synthetic dataset. Further, we demonstrate the advantage of the proposed method for estimating in-plane motion using the acquired projection data. Conclusion: Symmetry breakers in the human body are widely used for the detection of tumors or strokes. We provide a fast estimation of the symmetry plane, robust to outliers, by computing it directly from a set of projections. Further, by coupling the symmetry prior with epipolar consistency, we overcome inherent limitations in the estimation of in-plane motion.
CRIS-Team Scopus Importer:2019-08-06
Keywords: Cone-beam CT; Consistency conditions; Data completeness; Motion compensation; Symmetry; Tomographic reconstruction

Syntactic-prosodic labeling of large spontaneous speech data-bases - Batliner, Anton; Kompe, Ralf; KIESSLING, Andreas; Niemann, Heinrich; Nöth, Elmar (Proc. Int. Conf. on Spoken Language Processing, 1996)
In automatic speech understanding, the division of continuously running speech into syntactic chunks is a great problem. Syntactic boundaries are often marked by prosodic means. For the training of statistic models for prosodic boundaries large data-bases are necessary. For the German VERBMOBIL project (automatic speech-to-speech translation), we developed a syntactic-prosodic labeling scheme where two main types of boundaries (major syntactic boundaries and syntactically ambiguous boundaries) and some other special boundaries are labeled for a large VERBMOBIL spontaneous speech corpus. We compare the results of classifiers (multilayer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and pure syntactic labels. The main advantage of the rough syntactic-prosodic labels presented in this paper is that large amounts of data could be labeled within a short time. Therefore, the classifiers trained with these labels turned out to be superior (recognition rates of up to 96%).

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation - Mill, Leonid; Wolff, David; Gerrits, Nele; Philipp, Patrick; Kling, Lasse; Vollnhals, Florian; Ignatenko, Andrew; Jaremenko, Christian; Huang, Yixing; De Castro, Olivier; Audinot, Jean Nicolas; Nelissen, Inge; et al. (Small Methods, 2021)
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples.
CRIS-Team Scopus Importer:2021-05-14
Keywords: helium ion microscopy; image analysis; machine learning; nanoparticles; segmentation; toxicology

Systematic data analysis and data mining in CatWalk gait analysis by heat mapping exemplified in rodent models for neurodegenerative diseases - Timotius, Ivanna; Canneva, Fabio; Minakaki, Georgia; Moceri, Sandra; Plank, Anne-Christine; Casadei, Nicolas; Riess, Olaf; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn; von Hörsten, Stephan (Journal of Neuroscience Methods, 2019)
Background Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection. New methods In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson’s disease (PD) and Huntington disease (HD). Results The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences. Comparison with existing method Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported. Conclusions Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.
Keywords: Gait analysis; CatWalk system; Heat map; Parkinson’s disease; Huntington disease; Rodent models

System Theory - Fischer, Peter; Sembritzki, Klaus; Maier, Andreas (Medical Imaging Systems: An Introductory Guide, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.system

Taking endoscopy to a higher dimension: Computer Aided 3-D NOTES - Höller, Kurt Emmerich; Petrunina, Maria; Penne, Jochen; Schneider, Armin; Wilhelm, Dirk; Feußner, Hubertus; Hornegger, Joachim (Proceedings of the 4th Russian-Bavarian Conference on Biomedical Engineering, 2008)