2D/3D Registration for Motion Compensated Reconstruction in Cone-Beam CT of Knees Under Weight-Bearing Condition - Berger, Martin; Müller, Kerstin; Choi, Jang-Hwan; Aichert, André; Maier, Andreas; Fahrig, Rebecca (IFMBE Proceedings, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.2d3dre_8

2-D Interactive Scar Layer Visualization - Kurzendorfer, Tanja; Reiml, Sabrina; Brost, Alexander; Toth, Daniel; Panayiotou, Maria; Mountney, Peter; Steidl, Stefan; Maier, Andreas (IGIC 2017 - Abstract Book, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.2dinte

2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Roser, Philipp; Felsner, Lina; Preuhs, Alexander; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT - Karbole, Wenke; Ploner, Stefan; Won, Jungeun; Marmalidou, Anna; Takahashi, Hiroyuki; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2024)
Vision is essential for quality of life, but is threatened by visionimpairing diseases like age-related macular degeneration (AMD). A recently proposed biomarker potentially to distinguish normal aging from AMD is the gap visualized between the retinal pigment epithelium (RPE) and the Bruch’s membrane. Due to lack of automated processing, to date, this gap was only described sparsely in histologic data or on optical coherence tomography (OCT) B-scans. By segmenting the posterior RPE boundary automatically for the first time, we enable fully-automatic quantification of the thickness of this gap in vivo across whole volumetric OCT images. Our novel processing pipeline leverages advancements in motion correction, volumetric image merging, and high resolution OCT. A novel 3D boundary regression network named depth map regression network (DMR-Net) estimates the gap thickness in the volume. As 3D networks require full-volume ground truth boundary labels, which are labor-intensive, we developed a novel semi-automatic labeling approach to refine existing labels based on the visibility of the gap with minimal user input. We demonstrate thickness maps across a wide age range of healthy participants (23 – 79 years). The median absolute error in the test set is 0.161 μm, which is well below the axial pixel spacing (0.89 μm). For the first time, our results allow spatially resolved analysis to investigate pathologic deviations in normal aging and AMD.
CRIS-Team Scopus Importer:2024-03-29

3-D LGE-MRI Segmentation using a Random Forest Classifier and Dynamic Programming - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Steidl, Stefan; Maier, Andreas (ESMRMB 2017, 34th Annual Scientific Meeting, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.3dlgem

3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT - Maier, Jennifer; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca; Choi, Jang-Hwan (IEEE Access, 2021)
Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject’s knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.<br
Keywords: Automatic exposure control; C-arm cone-beam CT; detector saturation; lower extremity; overexposure artifacts; non-rigid reconstruction; X-ray imaging and computed tomography

3-D OCT Motion Correction Efficiently Enhanced with OCT Angiography - Ploner, Stefan; Kraus, Martin F.; Husvogt, Lennart; Moult, Eric M.; Alibhai, A. Yasin; Schottenhamml, Julia; Geimer, Tobias; Rebhun, Carl B.; Lee, Byungkun; Baumal, Caroline R.; Waheed, Nadia K.; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2018)

3-D Printing Based Production of Head and Neck Masks for Radiation Therapy Using Ct Volume Data: A Fully Automatic Framework - Chen, Shuqing; Lu, Yanye; Hopfgartner, Christian; Suehling, Michael; Steidl, Stefan; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dprin
Keywords: 3-D printing; immobilization mask; atlas registration

3-D Reconstruction of Historical Documents using an X-Ray C-Arm CT System - Stromer, Daniel; Christlein, Vincent; Anton, Gisela; Kugler, Patrick; Maier, Andreas (Proceedings of the 31th conference on Image and Vision Computing New Zealand 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dreco_2
Keywords: X-Ray imaging; Iron gall ink reconstruction; Historical Document Analysis

3-D Reconstruction of Iron Gall Ink Writings - Stromer, Daniel; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of 7th Conference on Industrial Computed Tomography (iCT 2017), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.3dreco_4

3D-Reconstruction of Stiff Wires from a Single Monoplane X-Ray Image - Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas (Informatik aktuell, 2019)
of preoperative data with intraoperative fluoroscopic images has been shown to reduce contrast agent, radiation dose and procedure time during endovascular repair of aortic aneurysms. However, the quality of the fusion may deteriorate due to often severe deformations of the vasculature caused by instruments such as stiff wires. To adapt the preoperative information intraoperatively to these deformations, the 3D positions of the inserted instruments are required. In this work, we propose a reconstruction method for stiff wires that requires only a single monoplane acquisition, keeping the impact on the clinical workflow to a minimum. To this end, the wire is segmented in the available X-ray image. To allow for a reconstruction in 3D, we then estimate a virtual second view of the wire orthogonal to the real projection based on vessel centerlines from a preoperative computed tomography. Using the real and estimated wire positions, we reconstruct the catheter using epipolar geometry. We achieve a mean modified Hausdorff distance of 4.1mm between the 3D reconstruction and the true wire course.
CRIS-Team Scopus Importer:2019-05-14

3D Tele-Medical Speech Therapy using Time-of-Flight Technology - Stürmer, Michael; Maier, Andreas; Schaller, Christian; Soutschek, Stefan; Penne, Jochen; Handschu, Rene; Scibor, Mateusz; Nöth, Elmar (4th European Congress for Medical and Biomedical Engineering, 2009)
Tele-medical applications allow the treatment of patients over long distances. This is of special importance in speech therapy, since speech therapists often operate in specialized centers for certain disorders. In this paper we propose a novel therapy system which employs Time-of-Flight (ToF) 3D cameras that acquire 3D surface data in real time. The distance data is compressed within a MPEG stream and transmitted to a remote client where a 3D visualization is implemented. The loss in accuracy compared to the original data is within 1.6 mm and 9.7 mm. The system can be used for real-time streaming.
Keywords: Speech Therapy; Time-of-Flight; Telemedicine; MPEG

3D Tensor Reconstruction in X-Ray Dark-Field Tomography: The First Phantom Result - Hu, Shiyang; Riess, Christian; Hornegger, Joachim; Fischer, Peter; Bayer, Florian; Weber, Thomas; Anton, Gisela; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)

3-D tracing of capillaries through three retinal vascular plexuses toward their closest superficial artery and vein in OCT angiography volumes - Ploner, Stefan; Schottenhamml, Julia; Moult, Eric M.; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2020)

4D-CT Reconstrucion Using Sparsity Level Constrained Compressed Sensing - Wu, Haibo; Maier, Andreas; Hofmann, Hannes; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

4D DSA Iterative Reconstruction - Endres, Jürgen; Rohkohl, Christopher; Schafer, Sebastian; Royalty, Kevin; Maier, Andreas; Kowarschik, Markus; Hornegger, Joachim (Proceedings of the Fully3D, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.4ddsai

A 2D dilated residual U-net for multi-organ segmentation in thoracic CT - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (CEUR Workshop Proceedings, 2019)
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neigh-bouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge.
CRIS-Team Scopus Importer:2019-05-03

A 3-D Projection Model for X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Maier, Andreas; Bopp, Johannes; Ludwig, Veronika; Anton, Gisela; Riess, Christian (Scientific Reports, 2019)

A 3-D Projection Model for X-ray Dark-field Imaging - Hu, Shiyang; Felsner, Lina; Maier, Andreas; Ludwig, Veronika; Anton, Gisela; Riess, Christian (2018)

A 3-D Scattering Model for Orientation-dependent X-ray Dark-field Imaging - Hu, Shiyang; Maier, Andreas; Hornegger, Joachim; Bayer, Florian; Weber, Thomas; Anton, Gisela; Riess, Christian (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)

A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery - Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph (35th European Photovoltaic Solar Energy Conference and Exhibition, 2018)
In this paper a dataset consisting of 2,426 solar cells extracted from high-resolution electroluminescence (EL) images is used for automated defect probability recognition. The collected images contain both functional and defective solar cells with varying degrees of degradation both in monocrystalline and polycrystalline solar modules. The images were labeled by expert who categorized the solar cells by the likelihood of a defect within each image. The labeled images can be used for development of computer vision and machine learning methods for automatic detection of different defects, like cracks, fracture interconnects, PID, and cell quality and for the purpose of predicting the power efficiency los
Keywords: EL Imaging; Machine Learning; Visual Inspection

Abstract: Are Fast Labeling Methods Reliable?: A Case Study of Computer-aided Expert Annotations on Microscopy Slides - Marzahl, Christian; Bertram, Christof A.; Aubreville, Marc; Petrick, Anne; Weiler, Kristina; Gläsel, Agnes C.; Fragoso, Marco; Merz, Sophie; Bartenschlager, Florian; Hoppe, Judith; Langenhagen, Alina; Jasensky, Anne Katherine; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT - Ploner, Stefan; Chen, Siyu; Won, Jungeun; Husvogt, Lennart; Breininger, Katharina; Schottenhamml, Julia; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2023)
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to < 9 s, fast enough for clinical use. We present a quantitative evaluation on 18 subjects with ocular pathology and demonstrate accurate correction during microsaccades. Transverse correction is limited only by ocular tremor, whereas submicron repeatability is achieved axially (0.51 μm median of medians), representing a dramatic improvement over previous work. This allows assessing longitudinal changes in focal retinal pathologies as a marker of disease progression or treatment response, and promises to enable multiple new capabilities such as supersampled/super-resolution volume reconstruction and analysis of pathological eye motion occuring in neurological diseases. This paper was accepted and presented at medical image computing and computer assisted intervention (MICCAI) 2022 [1].
CRIS-Team Scopus Importer:2023-09-29

Abstract: Automatic CAD-RADS Scoring using Deep Learning - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buß, Sebastian; Görich, Johannes; Sühling, Michael; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests - Chen, Shuqing; Stromer, Daniel; Alnasser Alabdalrahim, Harb; Schwab, Stefan; Weih, Markus; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Automatic Malignancy Estimation for Pulmonary Nodules from CT Images - Mentl, Katrin; Saffoury, Rimon; Maier, Andreas (Informatik Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.abstra_7

Abstract: Automatic Path Planning for Safe Guide Pin Insertion in PCL Reconstruction Surgery - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Bildverarbeitung für die Medizin, 2022)
Reconstruction surgery of torn ligaments requires anatomically correct fixation of the graft substitute on the bone surface. Several planning methodologies have been proposed to standardise the surgical workflow by localising drill sites or defining the drill tunnel orientation. A precise drill tunnel is of high clinical relevance to prevent detrimental changes in the reconstructed ligament’s biomechanics as well as early wall breakout with the risk of damaging neurovascular structures. Unfortunately, the practical implementation of these guidelines is limited by the often complex and time-consuming nature of the planning steps. In this work, we propose an automatic solution to support the trauma surgeon in guide pin path planning in double-bundle posterior cruciate ligament (PCL) reconstruction surgery on the lateral tibia. A two-stage algorithm is proposed that operates on a single intra-operative 2D X-ray image: First, key anatomic cues are extracted from the image using a multi-task deep learning algorithm. Then, these cues are forwarded to a geometric pipeline in which a logical partitioning of the bone contour is used to orient the drilling path optimally and ensure a safe distance between the drill tunnel and the bone edge. In contrast to a single-stage algorithm that directly calculates the tunnel positioning parameters, this allows the user to adjust the location of the inferred anatomical features interactively and enables low-latency modification of the path proposal. We evaluate the approach on 38 radiographs of the tibia where we observe a median path angulation error of 0.37° and a median localisation error of 0.96 mm for the ligament attachment centre. The results suggest further clinical validation and comparison to the accuracy of manual plannings.<br

Abstract: Coronary Artery Plaque Characterization from CCTA Scans Using DL and Radiomics - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (BVM Workshop 2020, 2020)
Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79–0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.84, the deep learning approach 0.86 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.<br

Abstract: Deep Learning Algorithms Out-perform Veterinary Pathologists in Detecting the Mitotically Most Active Tumor Region - Aubreville, Marc; Bertram, Christof A.; Marzahl, Christian; Gurtner, Corinne; Dettwiler, Martina; Schmidt, Anja; Bartenschlager, Florian; Merz, Sophie; Fragoso, Marco; Kershaw, Olivia; Klopeisch, Robert; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Deep Learning-based Quantification of Pulmonary Hemosiderophages in Cytology Slides - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Stayt, Jason; Jasensky, Anne Katherine; Bartenschlager, Florian; Fragoso, Marco; Barton, Ann K.; Elsemann, Svenja; Jabari, Samir; Krauth, Jens; Madhu, Prathmesh; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation - Gündel, Sebastian; Setio, Arnaud A.A.; Grbic, Sasa; Maier, Andreas; Comaniciu, Dorin (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: Fiducial Marker Recovery and Detection From Severely Truncated Data in Navigation-Assisted Spine Surgery - Fan, Fuxin; Kreher, Björn W.; Keil, Holger; Maier, Andreas; Huang, Yixing (Bildverarbeitung für die Medizin 2023, 2023)
Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone beam computed tomography (CBCT) systems. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method) [1]. For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained only on simulated data and the two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1 mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5 mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional data preparatio
Keywords: Data truncation; Deep learning; CBCT; Spine surgery; Fiducial marker

Abstract: Is Medical Chest X-ray Data Anonymous? - Packhäuser, Kai; Gündel, Sebastian; Münster, Nicolas; Syben, Christopher; Christlein, Vincent; Maier, Andreas (Informatik aktuell, 2023)

Abstract: Learning to avoid poor images: towards task-aware c-arm cone-beam ct trajectories - Zaech, Jan Nico; Gao, Cong; Bier, Bastian; Taylor, Russell; Maier, Andreas; Navab, Nassir; Unberath, Mathias (Informatik aktuell, 2020)
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol.
CRIS-Team Scopus Importer:2020-04-21

Abstract: Maximum A-posteriori Signal Recovery for OCT Angiography Image Generation - Husvogt, Lennart; Ploner, Stefan; Chen, Siyu; Stromer, Daniel; Schottenhamml, Julia; Alibhai, Yasin; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Pan-tumor CAnine CuTaneous Cancer Histology (CATCH) Dataset - Wilm, Frauke; Fragoso, Marco; Marzahl, Christian; Qiu, Jingna; Puget, Chloé; Diehl, Laura; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Informatik aktuell, 2023)
The identification of tumor regions on cutaneous tissue sections and the subsequent differentiation into individual tumor subtypes are routine tasks for veterinary pathologists. However, manual tumor delineation can be time-consuming and morphological similarities of tumor types can make the subtyping task difficult. Deep learning-based algorithms have been successfully applied for both tasks but require a large amount of annotated data to learn robust representations. Acquiring the annotations necessary to train these algorithms can be time-consuming and requires expert knowledge. Publicly available datasets reduce this annotation overhead and allow for a comparison of image analysis algorithms trained for the task at hand. We published a publicly available dataset of 350 whole slide images (WSIs) of seven different canine cutaneous tumors. These were annotated for the tumor subtypes and additionally six skin tissue classes. Overall, the database includes 12,424 polygon annotations for 13 classes, which exceeds most publicly available datasets in annotation extent and label diversity. In our dataset report, we validated the provided annotations through inter-rater experiments on a subset of the presented dataset, where three pathologists demonstrated high consistency in labeling tissue structures, especially for tumor annotations. Furthermore, we performed a technical validation by first training a deep neural network for tissue phenotyping. The model segmented the WSI into tumor and five skin tissue classes, where we achieved a class-averaged Jaccard coefficient of 0.7047 and a Jaccard coefficient of 0.9044 for tumor in particular. Afterward, we classified the segmented tumor regions into the tumor subtypes and achieved a slide-level accuracy of 0.9857. These baseline results provide a starting point for the development of more advanced algorithms. Previous works have shown that canine and human cutaneous tumors share various histologic patterns, which extends the added value of the presented dataset beyond veterinary pathology [1].
Created from Fastlane, Scopus look-up

Abstract: QuaSI - Quantile Sparse Image A Prior for Spatio-Temporal Denoising of Retinal OCT Data - Schirrmacher, Franziska; Köhler, Thomas; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Maier, Andreas (Informatik aktuell, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.abstra

Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Informatik Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.abstra_0

Abstract: 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 (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data - Thamm, Florian; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (BVM Workshop, 2021)
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).

Accelerating Multi-Echo Water-Fat MRI with a Joint Locally Low-Rank and Spatial Sparsity-Promoting Reconstruction - Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kiefer, Berthold; Hornegger, Joachim; Maier, Andreas (Magnetic Resonance Materials in Physics Biology and Medicine, 2016)
UnivIS-Import:2017-03-24:Pub.2016.tech.IMMD.IMMD5.accele
Keywords: Multi-echo Dixon; Quantitative water-fat MRI; Compressed sensing; Locally low-rank

Accuracy of Fatty Acid Quantification using Bipolar Multi-Echo MRI for Varying Numbers of Echoes - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.accura_9

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI - Schneider, Manuel; Janas, Gemini; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Magnetic Resonance in Medicine, 2019)
Purpose: To develop a bipolar multi-echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength.
CRIS-Team WoS Importer:2019-04-09

Accurate laser scanner to camera calibration with application to range sensor evaluation - Fürsattel, Peter; Plank, Claus; Maier, Andreas; Riess, Christian (IPSJ Transactions on Computer Vision and Applications, 2017)

A Comparative Error Analysis of Current Time-of-Flight Sensors - Fürsattel, Peter; Placht, Simon; Balda, Michael; Schaller, Christian; Hofmann, Hannes; Maier, Andreas; Riess, Christian (IEEE Transactions on Computational Imaging, 2016)

A comparison of methods for adapting 177Lu dose-voxel-kernels to tissue inhomogeneities - Götz, Theresa; Schmidkonz, Christian; Lang, E. W.; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Physics in Medicine and Biology, 2019)
In 177Lu radionuclide therapies, dosimetry is used for determining patient-individual dose burden. Standard approaches provide whole organ doses only. For assessing dose heterogeneity inside organs, voxel-wise dosimetry based on 3D SPECT/CT imaging could be applied. Often, this is achieved by convolving voxel-wise time-activity-curves with appropriate dose-voxel-kernels (DVK). The DVKs are meant to model dose deposition, and can be more accurate if modelled for the specific tissue type under consideration. In literature, DVKs are often not adapted to these inhomogeneities, or simple approximation schemes are applied. For 26 patients, which had previously undergone a 177Lu-PSMA or -DOTATOC therapy, decay maps, mass-density maps as well as tissue-type maps were derived from SPECT/CT acquisitions. These were used for a voxel-based dosimetry based on convolution with DVKs (each of size (4.8mm)3) obtained by four different DVK methods proposed in literature. The simplest only considers a spatially constant soft-tissue DVK (herein named 'constant'), while others either take into account only the local density of the center voxel of the DVK (herein named 'center-voxel') or scale each voxel linearly according to the proper mass density deduced from the CT image (herein named 'density') or considered both the local mass density as well as the direct path between the center voxel and any voxel in its surrounding (herein named 'percentage'). Deviations between resulting dose values and those from full Monte-Carlo simulations (MC simulations) were compared for selected organs and tissue-types. For each DVK method, inter-patient variability was considerable showing both under- and over-estimation of energy dose compared to the MC result for all tissue densities higher than soft tissue. In kidneys and spleen, 'constant' and 'density'-scaled DVKs achieved estimated doses with smallest deviations to the full MC gold standard (∼5%-8% underestimation). For low and high density tissue types such as lung and adipose or bone tissue, alternative DVK methods like 'center-voxel'- and 'percentage'- scaled achieved superior results, respectively. Concerning computational load, dose estimation with the DVK method 'constant' needs about 1.1 s per patient, center-voxel scaling amounts to 1.2 s, density scaling needs 1.4 s while percentage scaling consumes 860.3 s per patient. In this study encompassing a large patient cohort and four different DVK estimation methods, no single DVK-adaption method was consistently better than any other in case of soft tissue kernels. Hence in such cases the simplest DVK method, labeled 'constant', suffices. In case of tumors, often located in tissues of low (lung) or high (bone) density, more sophisticated DVK methods excel. The high inter-patient variability indicates that for evaluating new algorithms, a sufficiently large patient cohort needs to be involved.
CRIS-Team Scopus Importer:2020-02-21
Keywords: dose-voxel-kernel; dosimetry; SPECT/CT

A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research - Aubreville, Marc; Bertram, Christof A.; Donovan, Taryn A.; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Scientific Data, 2020)
Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.
CRIS-Team Scopus Importer:2020-12-04

A Completely Annotated Whole Slide Image Dataset of Canine Breast Cancer to Aid Human Breast Cancer Research - Aubreville, Marc; Bertram, Christof A.; Donovan, Taryn A.; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

A Cone-beam Reconstruction Algorithm for Dose-minimized Short Scan and Super Short Scan - Xia, Yan; Dennerlein, Frank; Bauer, Sebastian; Berger, Martin; Hornegger, Joachim; Maier, Andreas (Proceedings of the Third International Conference on Image Formation in X-Ray Computed Tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.aconeb_8
Keywords: GRK-1773

Acquisition Parameter–Conditioned Generative Adversarial Network for Enhanced MR Image Synthesis - Denck, Jonas; Ferguson, George William; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (28th Annual Meeting & Exhibition), 2020)

Acquisition Parameter Conditioned Magnetic Resonance Image-to-Image Translation - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Bildverarbeitung für die Medizin 2021, 2021)

Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation - Vesal, Sulaiman; Gu, Mingxuan; Kosti, Ronak Vijaypal; Maier, Andreas; Ravikumar, Nishant (IEEE transactions on medical imaging, 2021)
Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimisation, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.
CRIS-Team Scopus Importer:2021-04-19
Keywords: Adaptation models; Adversarial Learning; Cardiac Segmentation; Entropy; Entropy Minimisation; Image segmentation; Magnetic resonance imaging; Minimization; multi-modal Segmentation; Point-Clouds; Shape; Training; Unsupervised Domain Adaptation

Adaption of 3D Models to 2D X-Ray Images during Endovascular Abdominal Aneurysm Repair - Toth, Daniel; Pfister, Marcus; Maier, Andreas; Kowarschik, Markus; Hornegger, Joachim (18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I, 2015)
Endovascular aneurysm repair (EVAR) has been gaining popularity over open repair of abdominal aortic aneurysms (AAAs) in the recent years. This paper describes a distortion correction approach to be applied during the EVAR cases. In a novel workflow, models (meshes) of the aorta and its branching arteries generated from preoperatively acquired computed tomography (CT) scans are overlayed with interventionally acquired fluoroscopic images. The overlay provides an arterial roadmap for the operator, with landmarks (LMs) marking the ostia, which are critical for stent placement. As several endovascular devices, such as angiographic catheters, are inserted, the anatomy may be distorted. The distortion reduces the accuracy of the overlay. To overcome the mismatch, the aortic and the iliac meshes are adapted to a device seen in uncontrasted intraoperative fluoroscopic images using the skeletonbased as-rigid-as-possible (ARAP) method. The deformation was evaluated by comparing the distance between an ostium and the corresponding LM prior to and after the deformation. The central positions of the ostia were marked in digital subtraction angiography (DSA) images as ground truth. The mean Euclidean distance in the image plane was reduced from 19.81+/-17.14mm to 4.56+/-2.81 mm.
Keywords: computational geometry; as-rigid-as-possible; mesh deformation; abdominal aortic aneurysm; EVAR

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

A Deep-Learning-based Automated Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging - Yoon, Seung Su; Schmidt, Michaela; Rick, Manuela; Chitiboi, Teodora; Sharma, Puneet; Emrich, Tilman; Tillmanns, Christoph; Wetzl, Jens; Maier, Andreas (Society for Cardiovascular Magnetic Resonance, 2021)

A deep learning based pipeline for optical coherence tomography angiography - Liu, Xi; Huang, Zhiyu; Wang, Zhenzhou; Wen, Chenyao; Jiang, Zhe; Yu, Zekuan; Liu, Jingfeng; Liu, Gangjun; Huang, Xiaolin; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Journal of Biophotonics, 2019)
Optical coherence tomography angiography (OCTA) is a relatively new imaging modality that generates microvasculature map. Meanwhile, deep learning has been recently attracting considerable attention in image-to-image translation, such as image denoising, super-resolution and prediction. In this paper, we propose a deep learning based pipeline for OCTA. This pipeline consists of three parts: training data preparation, model learning and OCTA predicting using the trained model. To be mentioned, the datasets used in this work were automatically generated by a conventional system setup without any expert labeling. Promising results have been validated by in-vivo animal experiments, which demonstrate that deep learning is able to outperform traditional OCTA methods. The image quality is improved in not only higher signal-to-noise ratio but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. Schematic description of the deep learning based optical coherent tomography angiography pipeline.
CRIS-Team Scopus Importer:2019-07-12
Keywords: CNN; deep learning; OCT angiography

A digital twin to overcome long-time challenges in photovoltaics - Lüer, Larry; Peters, Ian Marius; Smith, Ana-Suncana; Dorschky, Eva; Eskofier, Björn; Liers-Bergmann, Frauke; Franke, Jörg; Sjarov, Martin; Brossog, Matthias; Guldi, Dirk Michael; Maier, Andreas; Brabec, Christoph (Joule, 2024)
The recent successes of emerging photovoltaics (PVs) are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant progress to match contradicting requirements, such as performance, longevity, and recyclability. In this perspective, we envision the layout of a digital twin for PV materials able to provide the necessary acceleration of innovation. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with physical models and digital twin concepts used in engineering. This layout will allow the use of high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships. We discuss the current state of the enabling technologies, namely fast scale-bridging surrogate models and large-scale optimization under uncertainty, and identify promising approaches for further development.<br
Keywords: digital twin; emerging photovoltaics; artificial intelligence; multi-scale simulations; optimization; high-throughput methods; materials acceleration platforms

A disease network-based deep learning approach for characterizing melanoma - Lai, Xin; Zhou, Jinfei; Wessely, Anja; Heppt, Markus; Maier, Andreas; Berking, Carola; Vera, Julio; Zhang, Le (International Journal of Cancer, 2021)
Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.
CRIS-Team Scopus Importer:2021-11-26
Keywords: autoencoder; disease network; genomics; melanoma; neural network; systems medicine

A Divide-and-Conquer Approach towards Understanding Deep Networks - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), 2019)
Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing. <
Keywords: Precision Learning; Debugging; CNN

Advanced Image Processing for Optical Coherence Tomography Angiography - Husvogt, Lennart; Maier, Andreas; Hornegger, Joachim (Proceedings of the 10th Joint Workshop of the German Research Training Groups in Computer Science, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.advanc

Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis - Folle, Lukas; Bayat, Sara; Kleyer, Arnd; Fagni, Filippo; Kapsner, Lorenz; Schlereth, Maja; Meinderink, Timo; Breininger, Katharina; Tascilar, Koray; Krönke, Gerhard; Uder, Michael; Sticherling, Michael; et al. (Rheumatology, 2022)
Objectives: To evaluate whether neural networks can distinguish between seropositive rheumatoid arthritis (RA), seronegative RA and psoriatic arthritis (PsA) based on inflammatory patterns from hand MRI and to test how psoriasis patients with subclinical inflammation fit into such patterns. Methods: ResNet neural networks were utilized to compare (i) seropositive RA vs. PsA, (ii) seronegative RA vs. PsA and (iii) seropositive vs. seronegative RA with respect to hand MRI data. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without presentation of demographic and clinical parameters. Additionally, the trained networks were applied to psoriasis patients without clinical arthritis. Results: MRI scans from 649 patients (135 seronegative RA, 190 seropositive RA, 177 PsA, 147 psoriasis) were fed into ResNet neural networks. AUROC was 75% for seropositive RA vs. PsA, 74% for seronegative RA vs. PsA and 67% for seropositive vs. seronegative RA. All MRI sequences were relevant for classification, however, when deleting contrast agent-based sequences the loss of performance was only marginal. The addition of demographic and clinical data to the networks did not provide significant improvements for classification. Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that a PsA-like MRI pattern may be present early in the course of psoriatic disease. Conclusion: Neural networks can be successfully trained to distinguish MRI inflammation related to seropositive RA, seronegative RA, and PsA.
Keywords: Rheumatoid arthritis; Psoriatic arthritis; neural networks; deep learning

Advanced volume rebuilding overcomes quilting, stretching, and banding image artifacts in orthogonally-scanned OCT - Ploner, Stefan; Won, Jungeun; Yaghy, Antonio; Lam, Kenneth; Girgis, Jessica M.; Schottenhamml, Julia; Husvogt, Lennart; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2023)

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

A Feasibility Study of Automatic Multi-Organ Segmentation Using Probabilistic Atlas - Chen, Shuqing; Endres, Jürgen; Dorn, Sabrina; Maier, Joscha; Lell, Michael; Kachelriess, Marc; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen., 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.afeasi

A Framework for Multiscale Quantitation of Relationships Between Choriocapillaris Flow Impairment and Geographic Atrophy Growth - Moult, Eric M.; Alibhai, A. Yasin; Lee, Byung Kun; Yu, Yue; Ploner, Stefan; Chen, Siyu; Maier, Andreas; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G. (American Journal of Ophthalmology, 2020)
Purpose: To develop a multiscale analysis framework for investigating the relationships between geographic atrophy (GA) growth rate and choriocapillaris (CC) blood flow impairment using optical coherence tomography (OCT) and OCT angiography (OCTA). Design: Retrospective case series. Methods: We developed an OCT/OCTA analysis framework that quantitatively measures GA growth rates at global and local scales and CC impairment at global, zonal, and local scales. A geometric GA growth model was used to measure local GA growth rates. The utility of the framework was demonstrated on 7 eyes with GA imaged at 2 time points using a prototype 400-kHz, 1050-nm swept-source OCTA system. Results: Qualitatively, there was a trend of increasing GA growth rate with increasing CC impairment. The local analysis model enabled growth rates to be estimated at each point on the GA boundary. However, there was no generally observed trend between local GA growth rates and local CC impairment. Conclusions: The global, zonal, and local analysis framework may be useful for investigating relationships between GA growth and CC impairment at different spatial scales. The geometric GA growth model enables spatially resolved growth measurements that capture the anisotropy of GA growth and may improve the characterization of GA progression.
CRIS-Team Scopus Importer:2020-03-27

A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography - Bögel, Marco; Hölter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015)
Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.
CRIS-Team Scopus Importer:2022-08-05

A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction - Gaffling, Simone; Daum, Volker; Steidl, Stefan; Maier, Andreas; Köstler, Harald; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2015)
Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.
UnivIS-Import:2015-03-09:Pub.2015.tech.IMMD.IMMD5.agauss
Keywords: 3-D histology; Gauss-Seidel; reconstruction; reference-free; registration

Age and Gender Recognition for Telephone Applications Based on GMM Supervectors and Support Vector Machines - Bocklet, Tobias; Maier, Andreas; Bauer, Josef; Burkhardt, Felix; Nöth, Elmar (Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008)

Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar (Proceedings Text, Speech and Dialogue; 11th International Conference, 2008)

Age Estimation on Panoramic Dental X-ray Images using Deep Learning - Wallraff, Sarah; Vesal, Sulaiman; Syben-Leisner, Christopher; Lutz, Rainer; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

A Gentle Introduction to Deep Learning in Medical Image Processing - Maier, Andreas; Syben-Leisner, Christopher; Lasser, Tobias; Riess, Christian (2018)

A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung] - Maier, Andreas; Syben-Leisner, Christopher; Lasser, Tobias; Riess, Christian (Zeitschrift für Medizinische Physik, 2019)
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.
CRIS-Team Scopus Importer:2019-05-03
Keywords: Computer-aided diagnosis; Deep learning; Image reconstruction; Image registration; Image segmentation; Introduction; Machine learning; Physical simulation

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging - Xiong, Zhaohan; Xia, Qing; Hu, Zhiqiang; Huang, Ning; Bian, Cheng; Zheng, Yefeng; Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas; Yang, Xin; Heng, Pheng Ann; Ni, Dong; et al. (Medical Image Analysis, 2021)
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.
CRIS-Team Scopus Importer:2020-11-13
Keywords: Convolutional neural networks; Image segmentation; Late gadolinium-enhanced magnetic resonance imaging; Left atrium

A Guided Spatial Transformer Network for Histology Cell Differentiation - Aubreville, Marc; Krappmann, Maximilian; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas (Eurographics Workshop on Visual Computing for Biology and Medicine, 2017)
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologis

A Hybrid Approach for Virtual Clinical Trials for Mammographic Imaging - Schebesch, Frank; Magdalena, Herbst; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proc. of SPIE, 2018)
Detection of lesions is an essential part of making a diagnosis in mammography and therefore is a main focus in the development of algorithms built for image quality assessment. We propose a hybrid approach with an accurate lesion projection model and embedding of lesions into clinical images that already contain relevant structures of anatomical noise. Using an algebraic lesion model, lesions with different sizes and contrasts are generated. The projection algorithm incorporates the modeling of blur effects due to system movement and the physical extent of the anode. Signal and background patches are extracted and used to evaluate channelized Hotelling observers with Laguerre-Gauss channels and with Gabor channels. A four-alternative forced-choice study with five medical imaging experts is performed and the inter-reader agreement with and without the model observers is determined by using Fleiss' kappa. Analyzing three different sizes for tiny, dense lesions and four density levels for larger mass-like lesions we find a good detection rate of the tiny lesions for both human as well as model observers. The inter-reader agreement using the common interpretation of Fleiss' kappa is substantial or better. Comparing full-field digital mammography and digital breast tomosynthesis w.r.t. the different mass densities we find that human readers and model observers perform well on the DBT data and the detection rate drops with lesion contrast as expected. The inter-reader agreement here is fair for the lowest contrast and substantial for the denser cases. Both human readers and model observers show difficulty in detecting the low contrast lesions in FFDM images. The inter-reader agreement is rather poor among all readers. Overall, the results indicate a good agreement between human observers and model observers and a distinctive benefit of 3-D reconstruction over FFDMs for low contrast lesions.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.ahybri_2
Keywords: model observer; digital breast tomosynthesis; mammography; image quality; lesion detection

A Joint Probabilistic Model for Speckle Variance, Amplitude Decorrelation and Interframe Variance (IFV) Optical Coherence Tomography Angiography - Ploner, Stefan; Riess, Christian; Schottenhamml, Julia; Moult, Eric M.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Proceedings des Workshops Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen, 2018)

A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy - Geimer, Tobias; Unberath, Mathias; Birlutiu, Adriana; Taubmann, Oliver; Wölfelschneider, Jens; Bert, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 2017)
In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With increasing surrogate complexity, we propose a respiratory motion estimation framework based on kernel ridge regression to cope with high-dimensional domains. This approach was validated on five patient datasets, consisting of a planning 4DCT and a follow-up 4DCT for each patient. Mean residual error was at best 2.73 ± 0.25 mm, but varied greatly between patients.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_4
Keywords: Motion Models; Range Imaging; Radiation Therapy; Kernel Trick; Active Shape Models

A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy - Geimer, Tobias; Birlutiu, Adriana; Unberath, Mathias; Taubmann, Oliver; Bert, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017 - Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regression. Evaluation was performed in a simulation study based on nine 4D CT patient data sets achieving a mean estimation error of 0.84 ± 0.21 mm for our approach.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_3

A Keypoint Detection and Description Network Based on the Vessel Structure for Multi-modal Retinal Image Registration - Sindel, Aline; Hohberger, Bettina; Fassihi Dehcordi, Sebastian; Mardin, Christian Y.; Lämmer, Robert; Maier, Andreas; Christlein, Vincent (Bildverarbeitung für die Medizin 2022, 2022)
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection and description network on small patches using a classification and a cross-modal descriptor loss function and apply the network to the full image size in the test phase. Our method demonstrates the best registration performance on our and a public multi-modal dataset in comparison to competing method

Alae Tracker: Tracking of the Nasal Walls in MR-Imaging - Breininger, Katharina; Maier, Andreas; Forman, Christoph; Flatz, Wilhelm; Messmer, Catalina; Schuster, Maria (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.alaetr
Keywords: tracking; nose; MRI

A language-independent feature set for the automatic evaluation of prosody - Maier, Andreas; Hönig, Florian Thomas; Zeißler, Viktor; Batliner, Anton; Körner, Erik; Yamanaka, Nobuyuki; Ackermann, Peter Donald; Nöth, Elmar (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)

A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor - Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Scientific Data, 2019)
We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.
Keywords: mitotic figure detection; tumor grading; digital pathology

A learning-based material decomposition pipeline for multi-energy x-ray imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang-Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2019)
PurposeBenefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks.
CRIS-Team WoS Importer:2019-03-12

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance - Thies, Mareike; Zäch, Jan Nico; Gao, Cong; Taylor, Russell; Navab, Nassir; Maier, Andreas; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2020)
Purpose: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. Methods: We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. Results: We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Conclusion: The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.
CRIS-Team Scopus Importer:2020-09-04
Keywords: Deep learning; Image-guided surgery; Metal artifact reduction; Tomographic reconstruction

Alzheimer's Detection from English to Spanish Using Acoustic and Linguistic Embeddings - Pérez Toro, Paula Andrea; Klumpp, Philipp; Hernandez, Abner; Arias Vergara, Tomás; Lillo, P.; Slachevsky, A.; García, A. M.; Schuster, M.; Maier, Andreas; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
Cross-lingual approaches are growing in popularity in the machine learning domain, where large amounts of data are required to obtain better generalizations. Moreover, one of the biggest problems is the availability of clinical speech data, where most of the resources are in English. For instance, not many available Alzheimer's Disease (AD) corpora in different languages can be found in the literature. Despite the phonological and phonemic differences between Spanish and English, fortunately, there are also similarities between these two languages, e.g., around 40% of all words in English have a related word in Spanish. In this work, we want to investigate the feasibility of combining information from English and Spanish languages to discriminate AD. Two datasets were considered: part of the Pitt Corpus, which is composed of English speakers, and a Spanish AD dataset composed of speakers from Chile. We based our analysis on known acoustic (Wav2Vec) and word (BERT, RoBERTa) embeddings using different classifiers. Strong language dependencies were found, even using multilingual representations. We observed that linguistic information was more important for classifying English AD (F-Score=0.76) and acoustic for Spanish AD (F-Score=0.80). Using knowledge transferred from English to Spanish achieved F-scores of up to 0.85 for discriminating AD.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Acoustic Embeddings; Alzheimer's Disease; Cross-Lingual Analysis; Linguistic Embeddings

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model - Zhong, Xia; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system’s iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose. Method To enable such functionality in a clinical context with sufficiently high accuracy, we present an extension of our previous patient surface model by adding internal anatomical landmarks associated with certain (main) bones of the human skeleton, in particular the spine. We also investigate different approaches to positioning of these landmarks employing CT datasets with annotated internal landmarks as training data. The general pipeline of our proposed method comprises the following steps: First, we train an active shape model using an existing avatar database and segmented CT surfaces. This stage also includes a gravity correction procedure, which accounts for shape changes due to the fact that the avatar models were obtained in standing position, while the CT data were acquired with patients in supine position. Second, we match the gravity-corrected avatar patient surface models to surfaces segmented from the CT datasets. In the last step, we derive the spatial relationships between the patient surface model and internal anatomical landmarks. Result We trained and evaluated our method using cross-validation using 20 datasets, each containing 50 internal landmarks. We further compared the performance of four different generalized linear models’ setups to describe the positioning of the internal landmarks relative to the patient surface. The best mean estimation error over all the landmarks was achieved using lasso regression with a mean error of 12.19 ± 6.98 mm. Conclusion Considering that interventional X-ray imaging systems can have detectors covering an area of about 200 mm × 266 mm (20 cm × 27 cm) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system
Keywords: Patient modeling; Anatomical landmark; Statistical shape model; Interventional X-ray Imaging

AMD-HookNet for Glacier Front Segmentation - Wu, Fei; Gourmelon, Nora; Seehaus, Thorsten; Zhang, Jianlin; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Transactions on Geoscience and Remote Sensing, 2023)
Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions; however, it is not feasible to perform this task manually for all calving glaciers globally due to time constraints. Deep-learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery. The calving front is represented as a single thin line between the ocean and the glacier, which makes the task vulnerable to inaccurate predictions. The limited availability of annotated glacier imagery leads to a lack of data diversity (not all possible combinations of different weather conditions, terminus shapes, sensors, etc. are present in the data), which exacerbates the difficulty of accurate segmentation. In this article, we propose attention-multihooking-deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework for synthetic aperture radar (SAR) images. The proposed method aims to enhance the feature representation capability through multiple information interactions between low-resolution and high-resolution inputs based on a two-branch U-Net. The attention mechanism, integrated into the two branch U-Net, aims to interact between the corresponding coarse and fine-grained feature maps. This allows the network to automatically adjust feature relationships, resulting in accurate pixel classification predictions. Extensive experiments and comparisons on the challenging glacier segmentation benchmark dataset CaFFe show that our AMD-HookNet achieves a mean distance error (MDE) of 438 m to the ground truth outperforming the current state of the art by 42%, which validates its effectivenes

A Memory Management Library for CT-Reconstruction on GPUs - Wu, Hao; Berger, Martin; Maier, Andreas; Lohmann, Daniel (Bildverarbeitung für die Medizin, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.amemor
Keywords: GRK-1773

A Microphone-independent Visualization Technique for Speech Disorders - Maier, Andreas; Wenhardt, Stefan; Haderlein, Tino; Schuster, Maria; Nöth, Elmar (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)

A Mixed Reality Simulation for Robotic Systems - Leipert, Martin; Sadowski, Jenny; Kießling, Michèle; Ngandeu, Emeric Kwemou; Maier, Andreas (Informatik aktuell, 2019)
In interventional angiography, kinematic simulation of robotic system prototypes in early development phases facilitates the detection of design errors. In this work, a game engine visualization with output is developed for such a robotic simulation. The goal of this is a better perception of the prototype by more realistic visualization. The achieved realism is evaluated in a user study. Additionally, the inclusion of real rooms walls into the simulations collision model is tested and evaluated, to verify smartglasses as a tool for interactive room planning. The walls are reconstructed from point clouds using a mean shift segmentation and RANSAC. Afterwards, the obtained wall estimates are ordered using a simple neighborhood graph.
CRIS-Team Scopus Importer:2021-04-20

A Multi-modal Registration and Visualization Software Tool for Artworks using CraquelureNet - Sindel, Aline; Maier, Andreas; Christlein, Vincent (Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 2023)
For art investigations of paintings, multiple imaging technologies, such as visual light photography, infrared reflectography, ultraviolet fluorescence photography, and x-radiography are often used. For a pixel-wise comparison, the multi-modal images have to be registered. We present a registration and visualization software tool, that embeds a convolutional neural network to extract cross-modal features of the crack structures in historical paintings for automatic registration. The graphical user interface processes the user's input to configure the registration parameters and to interactively adapt the image views with the registered pair and image overlays, such as by individual or synchronized zoom or movements of the views. In the evaluation, we qualitatively and quantitatively show the effectiveness of our software tool in terms of registration performance and short inference time on multi-modal paintings and its transferability by applying our method to historical prints.<br
Keywords: Multi-modal registration; Visualization; Convolutional neural networks

A multi-sensor architecture combining human pose estimation and real-time location systems for workflow monitoring on hybrid operating suites - Rodrigues, Vinicius F.; Antunes, Rodolfo S.; Seewald, Lucas A.; Bazo, Rodrigo; dos Reis, Eduardo S.; dos Santos, Uelison J.L.; Righi, Rodrigo da R.; da S., Luiz G.; da Costa, Cristiano A.; Bertollo, Felipe L.; Maier, Andreas; Eskofier, Björn; et al. (Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications, 2022)
Despite the advancements to improve patient safety, a significant number of errors still occur in Operating Suites (OS). To improve medical decision-making and the resulting quality of care, it is essential to monitor and understand medical activities’ workflow and interactions. Although some strategies employ different sensor devices, their focus is not on generating complete workflow information. They only combine different data sources to generate their final output, lacking at least one piece of information from a workflow. To tackle this challenge, this paper presents [Formula presented], a distributed architecture model for sensor data acquisition and processing. [Formula presented] ’s main contribution lies in its multi-sensor data fusion algorithms to extract a computational representation of activities in surgical procedures. In addition, [Formula presented] is flexible to accommodate different deployment configurations,combining depth cameras, Ultra-Wideband positioning systems,and deep learning-based human pose estimation (HPE) mechanisms. The workflow monitoring mechanism was deployed in an actual hybrid OS for an extensive evaluation of the proposal. Our experiments demonstrate that the architecture can capture the information required to monitor the surgical workflow. In particular, the proposed HPE methodology accurately detects the poses of medical staff members, with a maximum error of 5cm.
CRIS-Team Scopus Importer:2022-06-03
Keywords: Healthcare; Human pose estimation; Hybrid operating suite; Real-time location system; Workflow monitoring

A Multi-task Framework for Skin Lesion Detection and Segmentation - Vesal, Sulaiman; Patil, Shreyas Malakarjun; Ravikumar, Nishant; Maier, Andreas (OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, 2018)
Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is time consuming and subject to intra- and inter-observer variations among dermatologists. This underlines the need for an accurate and automatic approach to skin lesion segmentation. To tackle this issue, we propose a multi-task convolutional neural network (CNN) based, joint detection and segmentation framework, designed to initially localize the lesion and subsequently, segment it. A ‘Faster region-based convolutional neural network’ (Faster-RCNN) which comprises a region proposal network (RPN), is used to generate bounding boxes/region proposals, for lesion localization in each image. The proposed regions are subsequently refined using a softmax classifier and a bounding-box regressor. The refined bounding boxes are finally cropped and segmented using ‘SkinNet’, a modified version of U-Net. We trained and evaluated the performance of our network, using the ISBI 2017 challenge and the PH2 datasets, and compared it with the state-of-the-art, using the official test data released as part of the challenge for the former. Our approach outperformed others in terms of Dice coefficients (>0.93), Jaccard index (>0.88), accuracy (>0.96) and sensitivity (>0.95), across five-fold cross validation experiments.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.amulti_3

An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes - Thamm, Florian; Jürgens, Markus; Taubmann, Oliver; Thamm, Aleksandra; Rist, Leonhard; Ditt, Hendrik; Maier, Andreas (Biomedical Physics and Engineering Express, 2022)
During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. In previous work we proposed VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedure, as in the worst case whole hemispheres may not be present due to impeded perfusion. Hence, we extended the labeling mechanism for the cerebral arteries to identify occluded vessels. In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92 % and 95 %. To the best of our knowledge, ours is the first work to address labeling and occlusion detection at once, whereby a sensitivity of 67 % and a specificity of 81 % were obtained for the latter. VirtualDSA++ also automatically segments and models the intracranial system, which we further used in a deep learning driven follow up work. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features. Exemplary, we derive in detail, firstly, the interactive planning of vascular interventions like the mechanical thrombectomy and secondly, the interactive suppression of vessel structures that are not of interest in diagnosing strokes (like veins). We discuss both features as well as further possibilities emerging from the proposed concept.<br

Analog Non-Linear Transformation-Based Tone Mapping for Image Enhancement in C-arm CT - Shi, Lan; Berger, Martin; Bier, Bastian; Söll, Christopher; Röber, Jürgen; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas; Maier, Jennifer (IEEE Medical Imaging Conference (MIC), 2016)
Flat-Panel C-arm Computed Tomography (CT) suffers from pixel saturation due to the detector’s limited dynamic range. We describe a novel approach to analog, non-linear tone mapping (TM) for preventing detector saturation. An analog TM operator (TMO) applies a non-linear transformation in a CMOS sensor and its inverse TMO based on 14-bit digital raw data. This is done in order to prevent overexposure and to enhance image quality to 32 bits. The method was applied to the cases of low-contrast head imaging and to that of imaging both knees. Cone-beam projection data with and without overexposure was simulated for a 200° short-scan of the knees and a 360° full-scan of a Forbild head phantom. The results show an increased correlation coefficient of 0.99 compared to 0.96 for overexposed knee data and a higher low-contrast visibility (CC=0.99) compared to linear quantization (CC=0.97
elib2cris::1512001600,shi2016a
Keywords: GRK-1773

Analysis and visualization of sleep stages based on deep neural networks - Krauß, Patrick; Metzner, Claus; Joshi, Nidhi; Schulze, Holger; Traxdorf, Maximilian; Maier, Andreas; Schilling, Achim (Neurobiology of Sleep and Circadian Rhythms, 2021)
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.
CRIS-Team Scopus Importer:2021-03-19
Keywords: Artificial neural networks; Deep learning; Electroencephalography (EEG); Hypnodensity graphs; Multidimensional scaling (MDS); Polysomnography (PSG); Sleep cycle analysis; Sleep stage scoring

Analysis by adversarial synthesis - A novel approach for speech vocoding - Mustafa, Ahmed; Biswas, Arijit; Bergler, Christian; Schottenhamml, Julia; Maier, Andreas (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Classical parametric speech coding techniques provide a compact representation for speech signals. This affords a very low transmission rate but with a reduced perceptual quality of the reconstructed signals. Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate. However, such models suffer from a very slow signal generation mechanism due to their sample-by-sample modelling approach. In this work, we introduce a new methodology for neural speech vocoding based on generative adversarial networks (GANs). A fake speech signal is generated from a very compressed representation of the glottal excitation using conditional GANs as a deep generative model. This fake speech is then refined using the LPC parameters of the original speech signal to obtain a natural reconstruction. The reconstructed speech waveforms based on this approach show a higher perceptual quality than the classical vocoder counterparts according to subjective and objective evaluation scores for a dataset of 30 male and female speakers. Moreover, the usage of GANs enables to generate signals in one-shot compared to autoregressive generative models. This makes GANs promising for exploration to implement high-quality neural vocoders.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Deep learning; Generative adversarial networks; Neural vocoder; Speech coding

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods - Schilling, Achim; Tomasello, Rosario; Henningsen-Schomers, Malte R.; Zankl, Alexandra; Surendra, Kishore; Haller, Martin; Karl, Valerie; Uhrig, Peter; Maier, Andreas; Krauß, Patrick (Language, Cognition and Neuroscience, 2020)
In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.
CRIS-Team Scopus Importer:2020-08-21
Keywords: computational corpus linguistics; MEG/EEG; natural language processing (NLP); naturalistic continuous speech stimuli; neurobiology of language

Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 1: Characterization of Onset of Necking and Expert Evaluation - Affronti, Emanuela; Jaremenko, Christian; Merklein, Marion; Maier, Andreas (Materials, 2018)
In automotive manufacturing, high strength materials, and aluminum alloys are widely used to address the requirement of ensuring a lightweight car body and correspondingly, reducing pollution. In this context of complexity of materials and structures, an optimized process design with finite element analyses (FEA) is mandatory, as well as a correct definition of the material forming limits. For this purpose, in sheet metal forming, the forming limit curve (FLC) is used. The FLC is defined by the onset of necking. The standard evaluation method according to DIN EN ISO 12004-2 is based on the cross-section method and assumes that the failure occurs due to a clear localized necking. However, this approach has its limitations, specifically in the case of brittle materials that do not exhibit a distinct necking phase. To overcome this challenge, a pattern recognition-based evaluation is proposed. Although pattern recognition and machine learning techniques have been widely employed in the medical field, few studies have investigated them in the context of analyzing metal sheet forming limits. The application of pattern recognition in metal forming is subject to the exact definition of the forming behaviors. Thereby, it is challenging to relate patterns on the strain distribution during Nakajima tests with the onset of necking for the FLC determination. Thus, the first approach was based on the crack evaluation, since this class is well-defined. However, of substantial interest is the evaluation of the general material instabilities that precede failure. Therefore, in the present study, the analysis of the material behavior during stretching is conducted in order to characterize instability classes. The results of Nakajima tests are investigated using an optical measurement system. A conventional pattern recognition approach based on texture features, considering the outcomes of expert interviews for the definition of classes is used for the FLC determination. Moreover, an analysis of the validity of the supervised learning is conducted. The results show a good prediction of the onset of necking, even for high strength materials with a recall of up to 92%. Some deviations are observed in the determination of the diffuse necking. The discrepancies of the different experts' prognoses highlight the user-dependency of the FLC, suggesting further investigations with an data-driven approach, could be beneficial.
LFT Import::2019-07-29 (2319)
Keywords: Forming limit curve; Machine learning; Pattern recognition; Sheet metal forming

Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 2: Unsupervised Methodology and Application - Jaremenko, Christian; Affronti, Emanuela; Maier, Andreas; Merklein, Marion (Materials, 2018)
The forming limit curve (FLC) is used in finite element analysis (FEA) for the modeling of onset of sheet metal instability during forming. The FLC is usually evaluated by achieving forming measurements with optical measurement system during Nakajima tests. Current evaluation methods such as the standard method according to DIN EN ISO 12004-2 and time-dependent methods limit the evaluation range to a fraction of the available information and show weaknesses in the context of brittle materials that do not have a pronounced constriction phase. In order to meet these challenges, a supervised pattern recognition method was proposed, whose results depend on the quality of the expert annotations. In order to alleviate this dependence on experts, this study proposes an unsupervised classification approach that does not require expert annotations and allows a probabilistic evaluation of the onset of localized necking. For this purpose, the results of the Nakajima tests are examined with an optical measuring system and evaluated using an unsupervised classification method. In order to assess the quality of the results, a comparison is made with the time-dependent method proposed by Volk and Hora, as well as expert annotations, while validated with metallographic investigations. Two evaluation methods are presented, the deterministic FLC, which provides a lower and upper limit for the onset of necking, and a probabilistic FLC, which allows definition of failure quantiles. Both methods provide a necking range that shows good correlation with the expert opinion as well as the results of the time-dependent method and metallographic examinations.
LFT Import::2019-07-29 (2327)
Keywords: Forming limit curve; Machine learning; Pattern recognition; Sheet metal forming

ANALYSIS OF HYPERNASAL SPEECH IN CHILDREN WITH CLEFT LIP AND PALATE - Maier, Andreas; Reuss, Alexander; Hacker, Christian; Schuster, Maria; Nöth, Elmar (Text, Speech and Dialogue, 2008)

Analysis of Interventional Workflow Phases based on Image Classification - Maier, Jennifer; Schottenhamml, Julia; Madhu, Prathmesh; da Costa, Cristiano André; Maier, Andreas (65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), 2021)
Background: Interventional workflow analysis can help to increase the quality and efficiency of performed procedures, which are two important factors in the medical domain. A useful tool for analyzing medical workflow is video-based phase annotation of procedures, since the duration of certain phases can give evidence about issues in the processes in order to propose suggestions for improvement. We present an approach for classifying frames of intervention videos in order to label phases of these procedures. Methods: The videos in our dataset show interventional procedures recorded at different locations during the LINC meeting congresses. In each video, after an opening title the case is presented using slides, followed by the actual procedure. The procedure itself is a live catheter examination, a presentation of fluoroscopic images of a previously performed procedure, or a mixture of both. Occasionally, full screen images of the treating physicians are shown. Our data set consists of ten videos recorded at five locations A to E, where the number of videos recorded at the locations is A=3, B=3, C=2, D=1 and E=1. This low amount of data and the fact that some videos are compiled of recordings at different time points currently prevent a time sequence analysis for phase annotation. Instead, we extract one image per second, apply image classification, and define the phases of the videos based on the classes. All extracted video frames are manually assigned to one of the five classes opening (95), fluoroscopy (9376), fluoroscopy overview (196), humans (315), and presentation (1546). For an automatic classification of the images, we use a ResNet18 pre-trained on ImageNet data, since residual networks performed well on real world images and have shown positive results for transfer learning in the medical domain. The network is adapted to our task by training on a part of the available videos. The data set is split in training, validation and test set depending on the location in order to avoid overfitting on certain treating physicians. This results in 7044 frames for training (A, D and E), 1860 frames for validation (B), and 2624 frames for testing (C). The classified images are used to assign phase labels to the test videos. Results: The trained network predicted the correct class for 99% of the test images. Because of this high accuracy, video phases were assigned reliably with average time deviations of 1.10 ± 0.94 seconds at phase changes. Incorrectly classified images led to short jumps in the phase annotations, which could in most cases be removed by requiring a minimum phase length of 3 seconds. Conclusion: Our proposed method can classify frames of procedure videos with a high accuracy and enables an annotation of video phases. These phases are a first step towards interventional workflow analysis and could be improved by incorporating temporal information from more videos. Live procedures and previously recorded treatments are currently not differentiated, since the images in both cases are similar. Here, the fluoroscopy overview frames and the audio signal could give hints and will be included in a future analysi

Analysis of three-dimensional joint space of the tibiofemoral joint - Choi, Jang Hwan; McWalter, Emily J.; Pal, Saikat; Maier, Andreas; Gold, Garry E.; Fahrig, Rebecca (IEEE Nuclear Science Symposium Conference Record, 2013)
The purpose of this study was to quantify in vivo the differences in knee joint space volume between upright, weightbearing and supine, non-weightbearing conditions using C-arm computed tomography (CT) with sub-millimeter voxel size. © 2013 IEEE.
CRIS-Team Scopus Importer:2022-08-05
Keywords: Cone-beam CT; joint space width; osteoarthritis; tibiofemoral joint; weightbearing knee

Analysis of Vertical and Horizontal Circular C-Arm Trajectories - Maier, Andreas; Choi, Jang-Hwan; Keil, Andreas; Niebler, Christine; Sarmiento, Marily; Fieselmann, Andreas; Gold, Garry; Delp, Scott; Fahrig, Rebecca (Proc. SPIE Vol. 7961, 2011)

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

Analyzing relative blood flow speeds in choroidal neovascularization using variable interscan time analysis OCT angiography - Rebhun, Carl B.; Moult, Eric M.; Ploner, Stefan; Moreira-Neto, Carlos; Alibhai, A. Yasin; Schottenhamml, Julia; Lee, Byungkun; Choi, WooJhon; Rifai, Fareed A.; Tams, Mary; Husvogt, Lennart; Baumal, Caroline R.; et al. (Ophthalmology Retina, 2018)
Purpose Longitudinally visualizing relative blood flow speeds within choroidal neovascularization (CNV) may provide valuable information regarding the evolution of CNV and the response to vascular endothelial growth factor (VEGF) inhibitors. Design Retrospective, longitudinal case series conducted at the New England Eye Center. Participants Patients with either treatment-naïve or previously treated CNV secondary to neovascular age-related macular degeneration. Methods Optical coherence tomography angiography (OCTA) was performed using a 400-kHz, 1050-nm swept-source OCT system with a 5-repeat B-scan protocol. Variable interscan time analysis (VISTA) was used to compute relative flow speeds from pairs of B-scans having 1.5- and 3.0-ms separations; VISTA signals then were mapped to a color space for display. Main Outcome Measures Quantitative outcomes included OCTA-based area and volume measurements of CNV at initial and follow-up visits. Qualitative outcomes included VISTA OCTA analysis of relative blood flow speeds, along with analysis of contraction, expansion, densification, and rarefication of CNV. Results Seven eyes of 6 patients (4 women and 2 men) with neovascular age-related macular degeneration were evaluated. Two eyes were treatment naïve at the initial visit. Choroidal neovascularization in all eyes at each visit showed relatively higher flow speeds in the trunk, central, and larger vessels and lower flow speed in the small vessels, which generally were located at the periphery of the CNV complex. Overall, the CNV appeared to expand over time despite retention of good visual acuity in all patients. In the treatment-naïve patients, slower-flow-speed vessels contracted with treatment, whereas the larger vessels with higher flow speed remained constant. Conclusions Variable interscan time analysis OCTA allows for longitudinal observations of relative blood flow speeds in CNV treated with anti-VEGF intravitreal injections. A common finding in this study is that the main trunk and larger vessels seem to have relatively faster blood flow speeds compared with the lesions' peripheral vasculature. Moreover, an overall growth of chronically treated CNV was seen despite retention of good visual acuity. The VISTA framework may prove useful for developing clinical end points, as well as for studying hemodynamics, disease pathogenesis, and treatment response.
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.analyz
Keywords: vista; variable interscan time analysis; oct angiography; octa; oct; cnv

An anthropomorphic deformable phantom for brain shift simulation - Bayer, Siming; Wydra, Adrian; Zhong, Xia; Ravikumar, Nishant; Strumia, Maddalena; Schaffert, Roman; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2018)
The prominent soft tissue deformation during a neurosurgical procedure, the so called brain shift phenomenon, affects the accuracy of the surgery greatly. Although the feasibility of numerous intraoperative modalities is investigated for the brain shift compensation, another state-of-the-art interventional imaging modality – C-Arm CT is rarely studied in this context. Due to the lack of clinical data, a suitable anthropomorphic deformable phantom is indispensable. The purpose of this study is to describe and determine the characteristics of a multi-modal deformable brain phantom made out of polyurethane and ceramic composite bone material. The phantom is made of six parts: skin, skull, brain, blood vessels, ventricles and an inflatable tumor. Fluid can be filled into the blood vessels, the ventricles and the tumor in order to simulate the brain shift phenomenon. Such an anthropomorphic phantom is both visible with MR and C-Arm CT scanner. In order to evaluate the clinical relevance of our phantom, we induce deformations with different degrees by in- and deflated the embedded tumor in 5 steps. Both MR and C-Arm CT (cone beam CT) images are acquired in each step. The induced deformation is estimated by performing mono-modal deformable registration with symmetric normalization method (SyN) both on MR and cone beam CT (CBCT) acquisitions. The maximum displacement estimated with MR and CBCT pair is 29.6mm and 28.1mm. This result correlates the clinical findings.<br
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.ananth_9

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT - Dreizin, David; Goldmann, Florian; LeBedis, Christina; Boscak, Alexis; Dattwyler, Matthew; Bodanapally, Uttam; Li, Guang; Anderson, Stephan; Maier, Andreas; Unberath, Mathias (Journal of Digital Imaging, 2021)
Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher’s exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p = 0.40), but significantly higher for translational instability (85.0% versus 75.1, p = 0.0007). Mean inference time was < 0.1 s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p = 0.002–0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.
CRIS-Team Scopus Importer:2021-02-05
Keywords: Convolutional neural network; Deep learning; Pelvic fracture; Pelvic instability; Pelvic ring disruption; Tile classification

An automatic histogram-based initializing algorithm for K-means clustering in CT - Tian, Mengqiu; Yang, Qiao; Maier, Andreas; Schasiepen, Ingo; Maass, Nicole; Elter, Matthias (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)

An Automatic Screening Test for Preschool Children: Theory and Data Collection - Bocklet, Tobias; Winterholler, Cordula; Maier, Andreas; Schuster, Maria; Nöth, Elmar (Proceedings of WOCCI 2009, 2009)

An automatic version of a reading disorder test - Maier, Andreas; Hönig, Florian Thomas; Steidl, Stefan; Nöth, Elmar; Horndasch, Stefanie; Sauerhöfer, Elisabeth; Kratz, Oliver; Moll, Gunther (ACM Transactions on Speech and Language Processing, 2011)

An Automatic Version of the Post-Laryngectomy Telephone Test - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Toy, Hikmet; Rosanowski, Frank (Proc. Text, Speech and Dialogue; 10th International Conference, 2007)

A new approach to the evaluation of forming limits in sheet metal forming - Merklein, Marion; Maier, Andreas; Kinnstätter, Daniel; Jaremenko, Christian; Affronti, Emanuela (Key Engineering Materials, 2015)
UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.anewap

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

A new CT based approach to quantify adipose tissue in paraspinal muscle - Engelke, Klaus; Museyko, Oleg; Guenzel, Daniel; Maier, Andreas; Laredo, Jean-Denis (2018)
EVALuna2:36396

A New Scale Space Total Variation Algorithm for Limited Angle Tomography - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography), 2016)
This paper proposes a scale space total variation (ssTV) algorithm to reduce large scale streaks in limited angle tomography. The weighted total variation (wTV) algorithm is able to remove most small scale streaks. However, it fails to reduce larger streaks since total variation (TV) regularization is scale-dependent and may regard them as homogeneous areas. Derived from the wTV algorithm, the proposed ssTV algorithm applies wTV regularization on the image at different scales using down-sampling and up-sampling operations and thus can reduce streaks more effectively. Advantages of the ssTV algorithm are demonstrated on both 2-D numerical data and a 3-D clinical dataset.<br
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.anewsc_3
Keywords: limited angle tomography; total variation; scale space; streak artifacts

A NEW WEIGHTED ANISOTROPIC TOTAL VARIATION ALGORITHM FOR LIMITED ANGLE TOMOGRAPHY - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
This paper addresses streak reduction in limited angle tomography. The weighted total variation (wTV) algorithm is able to remove most Poisson noise and small streaks for clinical limited angle data. However, it is still not sufficient to remove larger streaks whose orientations are mostly dependent on the scan trajectory. We propose a new weighted anisotropic total variation (waTV) algorithm, which uses four neighboring pixels to calculate the gradient along the streaks' normal direction and is able to reduce large streaks well.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.anewwe
Keywords: limited angle tomography; streak artifacts; Total variation

An Extension to the Sammon Mapping for the Robust Visualization of Speaker Dependencies - Maier, Andreas; Exner, Julian; Steidl, Stefan; Batliner, Anton; Haderlein, Tino; Nöth, Elmar (Text, Speech and Dialogue, 2008)

ANIMAL‐SPOT enables animal‐independent signal detection and classification using deep learning - Bergler, Christian; Smeele, Simeon Q.; Tyndel, Stephen A.; Barnhill, Alexander; Ortiz, Sara T.; Kalan, Ammie K.; Cheng, Rachael Xi; Brinkløv, Signe; Osieka, Anna N.; Tougaard, Jakob; Jakobsen, Freja; Wahlberg, Magnus; et al. (Scientific Reports, 2022)
Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human- annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience.<br

An improved combination of ordered subsets and momentum for fast model-based iterative CT reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2020)
Model-based iterative CT reconstruction suffers from its high computational cost. To increase the reconstruction speed, algorithms using a momentum acceleration can be combined with the ordered subset approach. As shown in previous works, the incorporation of ordered subsets strongly improves the reconstruction time but makes the outcome of the reconstruction difficult to predict due to its empirical nature. Specifically, when the number of subsets is too high, divergence or convergence to an unsatisfactory result can easily appear. In this work, we propose a new combination of ordered subsets with momentum to achieve fast reconstruction in a more robust manner. Our approach, referred to as EOS-MFISTA, is an Efficient Ordered Subset strategy based on the steps of MFISTA, a monotonic version of FISTA. In short, EOS-MFISTA uses objective function values to decide on the next update step and does this with little increase in computational effort. The performance of EOS-MFISTA is evaluated on real CT data of a head phantom and a human chest. Starting from a zero image, 200 iterations of EOS-MFISTA are calculated and compared with other algorithms. Quantitative analyses based on the RMSE show that EOS-MFISTA is much more robust than OS-FISTA, especially when the number of subsets is increased to accelerate reconstruction. Visual inspection of results obtained with 50 iterations further supports these findings.
CRIS-Team Scopus Importer:2020-06-30
Keywords: Computed tomography; FISTA; Iterative reconstruction; MFISTA; Ordered subsets

An Improved Extrapolation Scheme for Truncated CT Data Using 2D Fourier-Based Helgason-Ludwig Consistency Conditions - Xia, Yan; Berger, Martin; Bauer, Sebastian; Hu, Shiyang; Aichert, André; Maier, Andreas (International Journal of Biomedical Imaging, 2017)
We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency conditions that mathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier representation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallel-beam and fan-beam imaging geometry. The derivation result indicates that there is a zero energy region forming a double-wedge shape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature. The major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform (FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the parameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be used to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data. Results show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method.
UnivIS-Import:2018-09-05:Pub.2017.tech.IMMD.IMMD5.animpr_2
Keywords: Truncation; CT; Helgason-Ludwig; Fourier-Consistency; Consistency Conditions

An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process - Bayer, Siming; Spiske, Ute; Geimer, Tobias; Luo, Jie; Wells III, William M.; Ostermeier, Martin; Fahrig, Rebecca; Arya, Nabavi; Bert, Christoph; Eyüpoglu, Ilker Yasin; Maier, Andreas (Algorithmen – Systeme – Anwendungen. Proceedings des Workshops, 2020)
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.<br

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy - Fischer, Peter; Faranesh, Anthony; Pohl, Thomas; Maier, Andreas; Rogers, Toby; Ratnayaka, Kanishka; Lederman, Robert; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2018)
In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.anmrba
Keywords: mri; x-ray imaging; motion compensation; electrocardiography; motion model

Anomaly detection in IR images of PV modules using supervised contrastive learning - Bommes, Lukas; Hoffmann, Mathis; Buerhop-Lutz, Claudia; Pickel, Tobias; Hauch, Jens; Brabec, Christoph; Maier, Andreas; Peters, Ian Marius (Progress in Photovoltaics, 2022)
Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample train and test data from the same distribution ignoring the presence of domain shift between data of different PV plants. Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labeled data of one source PV plant and make predictions on another target plant. We train a ResNet-34 convolutional neural network with a supervised contrastive loss, on top of which we employ a k-nearest neighbor classifier to detect anomalies. Our method achieves a satisfactory area under the receiver operating characteristic (AUROC) of 73.3% to 96.6% on nine combinations of four source and target datasets with 2.92 million IR images of which 8.5% are anomalous. It even outperforms a binary cross-entropy classifier in some cases. With a fixed decision threshold, this results in 79.4% and 77.1% correctly classified normal and anomalous images, respectively. Most misclassified anomalies are of low severity, such as hot diodes and small hot spots. Our method is insensitive to hyperparameter settings, converges quickly, and reliably detects unknown types of anomalies making it well suited for practice. Possible uses are in automatic PV plant inspection systems or to streamline manual labeling of IR datasets by filtering out normal images. Furthermore, our work serves the community with a more realistic view on PV module fault detection using unsupervised domain adaptation to develop more performant methods with favorable generalization capabilities.
CRIS-Team WoS Importer:2022-04-08

A Novel Filtered Backprojection-Based Algorithm for Sparse View CT Image Reconstruction - Wu, Meng; Maier, Andreas; Yang, Qiao; Fahrig, Rebecca (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.anovel_3

A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data - Jordan, Johannes Michael; Angelopoulou, Elli; Maier, Andreas (Journal of Electrical and Computer Engineering, 2016)
Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.
CRIS-Team Scopus Importer:2022-08-05

A Novel Method for Contact-Free Cardiac Synchronization Using the Pilot Tone Navigator - Schroeder, Lea; Wetzl, Jens; Maier, Andreas; Lauer, Lars; Bollenbeck, Jan; Fenchel, Matthias; Speier, Peter (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.anovel

A Novel Trilateral Filter for Digital Subtraction Angiography - Tripathi, Purvi; Obler, Richard; Maier, Andreas; Janssen, Hendrik (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

A phantom study on dose efficiency for orthopedic applications: Comparing slot-scanning radiography using ultra-small-angle tomosynthesis to conventional radiography - Luckner, Christoph; Weber, Thomas; Herbst, Magdalena; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Medical Physics, 2020)
Purpose: This paper studies the abilities of a twin-robotic x-ray slot-scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography. Methods: We investigate the dose saving capabilities, especially in terms of the signal- and the contrast-to-noise ratio, as well as the scatter-to-primary ratio of the proposed slot-scanning method in comparison to the state-of-the-art method for length-extended imaging. As a baseline, we use x-ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose. To obtain an estimate of the photon-related noise directly from an x-ray image, we implement a Poisson-Gaussian noise model. This model is used to compare the dose efficiency of two settings and combined with the well-known (Formula presented.) to determine the transmission parameters. We present a method with an associated measurement protocol, utilizing the robotic capabilities of the used system to automatically obtain quasi-scatter-free ground-truth data with exact geometric correspondence to full-field and slot acquisitions. In total, we investigate two body regions (thoracic spine and lumbar spine) in anterior-posterior view with two patient sizes (BMI = 22 and 30) in two acquisition modes (conventional and slot scan with a flat-panel detector) with and without anti-scatter grid using an anthropomorphic upper-body phantom. Results: We have shown that it is feasible to combine the proposed approach with the (Formula presented.) for the determination of scatter rejection parameters. The use of an anti-scatter grid is indicated for full-field acquisitions allowing for dose savings up to 46% compared to their gridless counterparts. When changing the acquisition mode to the investigated slot scan, the use of an anti-scatter grid has no major impact on the image quality in terms of dose efficiency, in particular for patients with a BMI of 22. However, an increased contrast improvement factor was found. For normal-sized patients, up to 53% of dose can be saved additionally in comparison to full-field acquisitions with grid. Moreover, we could demonstrate that a slot size of 5 cm and air gap of 10 cm is sufficient to achieve scatter-to-primary ratios, which are equal or better compared to those of the full-field acquisitions with a grid. Conclusions: We have shown, that the slot-scanning approach is always superior to the conventional full-field acquisition in terms of signal-to-noise and scatter-to-primary ratios. Compared to the state-of-the-art acquisition protocols with a grid, dose savings up to 53% are possible due to the scatter rejection without compromising the SNR. Hence, the use of the slot-scanning method is indicated, especially when it comes to regularly carried-out follow-up acquisitions, for example, in the case of scoliosis monitoring.
CRIS-Team Scopus Importer:2021-04-19
Keywords: dose; orthopedics; slot-scanning; SNR; tomosynthesis

Appearance Learning for Image-based Motion Estimation in Tomography - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Hoppe, Elisabeth; Huang, Yixing; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)

Application of Corneal Optical Coherence Tomography Angiography for Assessment of Vessel Depth in Corneal Neovascularization - Nanji, Afshan; Redd, Travis; Chamberlain, Winston; Schallhorn, Julie M.; Chen, Siyu; Ploner, Stefan; Maier, Andreas; Fujimoto, James G.; Jia, Yali; Huang, David; Li, Yan (Cornea, 2020)
PURPOSE: To map and measure the depths of corneal neovascularization (NV) using 3-dimensional optical coherence tomography angiography (OCTA) at 2 different wavelengths. METHODS: Corneal NV of varying severity, distribution, and underlying etiology was examined. Average NV depth and vessel density were measured using 840-nm spectral-domain OCTA and 1050-nm swept-source OCTA. The OCTA results were compared with clinical slit-lamp estimation of NV depth. RESULTS: Twelve eyes with corneal NV from 12 patients were imaged with OCTA. Clinically "superficial," "midstromal," and "deep" cases had an average vessel depth of 23%, 39%, and 66% on 1050-nm OCTA, respectively. Average vessel depth on OCTA followed a statistically significant ordinal trend according to the clinical classification of vessel depth (Jonckheere-Terpstra test, P < 0.001). In 8 cases where both 840-nm OCTA and 1050-nm OCTA were acquired, there was excellent agreement in the mean vessel depth between the 2 systems (concordance correlation coefficient = 0.94, P < 0.001). The average vessel density measured by 840-nm OCTA was higher (average 1.6-fold) than that measured by 1050-nm OCTA. CONCLUSIONS: Corneal OCTA was able to map corneal NV in 3 dimensions and measure vessel depth and density. The depth of corneal NV varied between different pathologies in a manner consistent with previous pathologic studies. The measured vessel density appeared to be affected by the interscan time, which affects blood flow velocity sensitivity, and the wavelength, which affects the ability to penetrate through opacity. These findings suggest possible clinical applications of OCTA for the diagnosis of corneal pathology and quantitative monitoring of therapeutic response in patients with corneal NV.
CRIS-Team Scopus Importer:2020-04-14

Application of deep learning algorithms for Lithographic mask characterization - Woldeamanual, Dereje; Erdmann, Andreas; Maier, Andreas (Proceedings of SPIE - The International Society for Optical Engineering, 2018)
The appearance of defects on the photomask is a key challenge in lithographic printing. Printable defects affect the lithographic process by causing errors in both the phase and magnitude of the light and of the sizes and location of the printed features. Presently 193 nm optical inspection tools are still the main ones for detecting pattern defects on EUV masks.1 However, pattern sizes on EUV masks could not be detected due to the resolution limit of 193 nm inspection tools. We propose and investigate the application of Convolutional Neural Networks (CNNs) to characterize and classify defects on lithographic masks. This paper details the training and evaluation of the CNNs to classify defects in simulated aerial images of an EUV setting. The simulation software Dr.LiTHO is used to simulate aerial images of defect-free masks and of masks with different types and locations of defects. Specifically we compute images of regular arrays of squares to be imaged with typical settings of EUV lithography (λ = 13.5 nm, NA= 0.33). We consider five types of absorber defects (extrusion, intrusion, oversize, undersize and center spot). The architecture of the CNN contains 4 convolutional layers (conv. layers) with a mixed size of filter,(3x3) and (5x5). The convolution stride and the spatial padding is 1 pixel for all conv. layers. Spatial pooling is carried out by 4 max-pooling layers. Two separate networks are trained for detection of the defect types and location, whereas a third algorithm combines the results. When an image is presented to the implemented algorithm and trained networks, it will return the defect type with its location. An accuracy of 99.9% on the training set and 99.3% on the test set is achieved for detection of the defect type. The network trained for location detection results in 98.7% training accuracy and 98.0% for the test set. Having a sufficient amount of training images the trained CNNs classify the types of defects and their location in the aerial image with high accuracy. The proposed method can be also applied to other defect types and simulation settings.
Created from Fastlane, Scopus look-up
Keywords: aerial images; Convolutional neural network; deep learning; EUV; Lithographic Mask; mask defects

Applying Deep Learning Algorithms to EL-images for Predicting the Module Power - Buerhop-Lutz, Claudia; Hoffmann, Mathis; Reeb, Luca; Pickel, Tobias; Hauch, Jens; Maier, Andreas (Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, 2019)
EL-images nicely visualize the cell quality of PV-modules in terms of existing failures like cell cracks and cell fractures. Nonetheless, the benefit of EL-images recorded in the field increases if a direct prediction of the module power is enabled. For the first time, deep learning algorithms are applied to predict the module power using EL-images. The dataset consists of more than 600 EL-images from different crystalline PV module manufactures. Regarding the network architecture, ResNet18 was identified to perform best with respect to computational time and accuracy. The predicted powers are quite accurate: the mean absolute error over the entire data set is 4.57 W. In addition, 72% of the module powers are predicted with a high accuracy of less than 2% error. Finally, it should be emphasized that even the EL-images recorded in the field with perspective distortion and motion blur are suitable for excellent predictions
Keywords: solar module; power; deep learning; regression; electroluminescence imaging

Applying nnU-Net to the segmentation of microaneurysms in OCTA data in patients with non-proliferative diabetic retinopathy - Husvogt, Lennart; Abu-Qamar, Omar; Levine, Emily; Schottenhamml, Julia; Ploner, Stefan; Breininger, Katharina; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (ARVO Annual Meeting 2021, 2021)

Approximate path seeking for statistical iterative reconstruction - Wu, Meng; Yang, Qiao; Maier, Andreas; Fahrig, Rebecca (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2015)
Statistical iterative reconstruction (IR) techniques have demonstrated many advantages in X-ray CT reconstruction. The statistical iterative reconstruction approach is often modeled as an optimization problem including a data fitting function and a penalty function. The tuning parameter values that regulate the strength of the penalty function are critical for achieving good reconstruction results. However, appropriate tuning parameter values that are suitable for the scan protocols and imaging tasks are often difficult to choose. In this work, we propose a path seeking algorithm that is capable of generating a series of IR images with different strengths of the penalty function. The path seeking algorithm uses the ratio of the gradients of the data fitting function and the penalty function to select pixels for small fixed size updates. We describe the path seeking algorithm for penalized weighted least squares (PWLS) with a Huber penalty function in both the directions of increasing and decreasing tuning parameter value. Simulations using the XCAT phantom show the proposed method produces path images that are very similar to the IR images that are computed via direct optimization. The root-mean- squared-error of one path image generated by the proposed method relative to full iterative reconstruction is about 6 HU for the entire image and 10 HU for a small region. Different path seeking directions, increment sizes and updating percentages of the path seeking algorithm are compared in simulations. The proposed method may reduce the dependence on selection of good tuning parameter values by instead generating multiple IR images, without significantly increasing the computational load.
CRIS-Team Scopus Importer:2022-08-05
Keywords: CT; Iterative reconstruction; Path seeking

Approximate truncation robust computed tomography - ATRACT - Dennerlein, Frank; Maier, Andreas (Physics in Medicine and Biology, 2013)

Approximation der Projektionsmatrizen einer C-Bogen 3D-Fahrt anhand der Odometriedaten - Stromer, Daniel; Maier, Andreas (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.approx
Keywords: Calibration; C-arm

Approximation of the projection matrices of a C-arc 3D-drive based on the odometry data Approximation der projektionsmatrizen einer C-Bogen 3D-Fahrt anhand der odometriedaten - Stromer, Daniel; Maier, Andreas (Informatik aktuell, 2014)
CRIS-Team Scopus Importer:2022-08-05

A practical statistical polychromatic image reconstruction for computed tomography using spectrum binning - Wu, Meng; Yang, Qiao; Maier, Andreas; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2014, 2014)
Polychromatic statistical reconstruction algorithms have very high computational demands due To The difficulty of The optimization problems and The large number of spectrum bins. We want To develop a more practical algorithm That has a simpler optimization problem, a faster numerical solver, and requires only a small amount of prior knowledge. In This paper, a modified optimization problem for polychromatic statistical reconstruction algorithms is proposed. The modified optimization problem utilizes The idea of determining scanned materials based on a first pass FBP reconstruction To fix The ratios between photoelectric and Compton scattering components of all image pixels. The reconstruction of a density image is easy To solve by a separable quadratic surrogate algorithm That is also applicable To The multi-material case. In addition, a spectrum binning method is introduced so That The full spectrum information is not required. The energy bins sizes and attenuations are optimized based on The True spectrum and object. With These approximations, The expected line integral values using only a few energy bins are very closed To The True polychromatic values. Thus both The problem size and computational demand caused by The large number of energy bins That are Typically used To model a full spectrum are reduced. Simulation showed That Three energy bins using The generalized spectrum binning method could provide an accurate approximation of The polychromatic X-ray signals. The average absolute error of The logarithmic detector signal is less Than 0.003 for a 120 kVp spectrum. The proposed modified optimization problem and spectrum binning approach can effectively suppress beam hardening artifacts while providing low noise images. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.apract
Keywords: Beam hardening artifact; CT; Iterative reconstruction; Polychromatic X-ray; Spectrum binning

A Preliminary Investigation of In Vivo Time-dependent 3D Morphology of Articular Cartilage of the Tibiofemoral Joint - Choi, Jang-Hwan; McWalter, Emily; Datta, Sanjit; Müller, Kerstin; Maier, Andreas; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca (ISMRM Workshop on Imaging Based Measures of Osteoarthritis, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.apreli

A principled representation of elongated structures using heatmaps - Kordon, Florian Johannes; Stiglmayr, Michael; Maier, Andreas; Martín Vicario, Celia; Pertlwieser, Tobias; Kunze, Holger (Scientific Reports, 2023)
The detection of elongated structures like lines or edges is an essential component in semantic image analysis. Classical approaches that rely on significant image gradients quickly reach their limits when the structure is context-dependent, amorphous, or not directly visible. This study introduces a principled mathematical description of elongated structures with various origins and shapes. Among others, it serves as an expressive operational description of target functions that can be well approximated by Convolutional Neural Networks. The nominal position of a curve and its positional uncertainty are encoded as a heatmap by convolving the curve distribution with a filter function. We propose a low-error approximation to the expensive numerical integration by evaluating a distance-dependent function, enabling a lightweight implementation with linear time complexity. We analyze the method’s numerical approximation error and behavior for different curve types and signal-to-noise levels. Application to surgical 2D and 3D data, semantic boundary detection, skeletonization, and other related tasks demonstrate the method’s versatility at low errors.<br

'APSifyed' Bruch's membrane opening (BMO) based peripapillary OCT-A analysis - Mardin, Christian Y.; Schottenhamml, Julia; Dehcordi, Sebastian Fassihi; Müller, Meike; Maier, Andreas; Hohberger, Bettina (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2021)
CRIS-Team WoS Importer:2021-10-01

A Realistic Digital Phantom for Perfusion C-Arm CT Based on MRI Data - Aichert, André; Manhart, Michael; Navalpakkam, Bharath; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
CTP is an important imaging modality for diagnosis of ischemic stroke, which is computed from of a series of consecutive CT-scans during the injection of contrast agent. Contrast flow at any point in space can be tracked as minor changes in intensity over a period of about 40 seconds to one minute, represented as a time-attenuation curve (TAC) for every voxel. This work presents an isotropic, dense, physiologically realistic and dynamic brain phantom for CT perfusion. The phantom is based on MRI scans of a volunteer and is freely available for download. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.areali_2

Are fast labeling methods reliable? a case study of computer-aided expert annotations on microscopy slides - Marzahl, Christian; Bertram, Christof A.; Aubreville, Marc; Petrick, Anne; Weiler, Kristina; Gläsel, Agnes C.; Fragoso, Marco; Merz, Sophie; Bartenschlager, Florian; Hoppe, Judith; Langenhagen, Alina; Jasensky, Anne Katherine; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Deep-learning-based pipelines have shown the potential to revolutionalize microscopy image diagnostics by providing visual augmentations and evaluations to a trained pathology expert. However, to match human performance, the methods rely on the availability of vast amounts of high-quality labeled data, which poses a significant challenge. To circumvent this, augmented labeling methods, also known as expert-algorithm-collaboration, have recently become popular. However, potential biases introduced by this operation mode and their effects for training deep neuronal networks are not entirely understood. This work aims to shed light on some of the effects by providing a case study for three pathologically relevant diagnostic settings. Ten trained pathology experts performed a labeling tasks first without and later with computer-generated augmentation. To investigate different biasing effects, we intentionally introduced errors to the augmentation. In total, the pathology experts annotated 26,015 cells on 1,200 images in this novel annotation study. Backed by this extensive data set, we found that the concordance of multiple experts was significantly increased in the computer-aided setting, versus the unaided annotation. However, a significant percentage of the deliberately introduced false labels was not identified by the experts.
CRIS-Team Scopus Importer:2020-10-30
Keywords: Computer-aided labelling; Expert-algorithm collaboration; Microscopy; Pathology

A Reliability Measure for Merging Data from Multiple Cameras in Optical Motion Correction - Ehrl, Jakob; Maclaren, Julian; Aksoy, Murat; Maier, Andreas; Bammer, Roland (Proc. ISMRM SCIENTIFIC WORKSHOP - Motion Correction in MRI 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.arelia
Keywords: motion correction; MRI; reliability

Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels - Bertram, Christof A.; Veta, Mitko; Marzahl, Christian; Stathonikos, Nikolas; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge. Additional to manual mitotic figure screening, we used a novel, algorithm-aided labeling process, that allowed to minimize the risk of missing rare mitotic figures in the images. All potential mitotic figures were independently assessed by two pathologists. The novel, publicly available set of labels contains 1,999 mitotic figures (+28.80%) and additionally includes 10,483 labels of cells with high similarities to mitotic figures (hard examples). We found significant difference comparing scores between the original label set (0.549) and the new alternative label set (0.735) using a standard deep learning object detection architecture. The models trained on the alternative set showed higher overall confidence values, suggesting a higher overall label consistency. Findings of the present study show that pathologists-defined labels may vary significantly resulting in notable difference in the model performance. Comparison of deep learning-based algorithms between independent datasets with different labeling methods should be done with caution.
CRIS-Team Scopus Importer:2020-10-30
Keywords: Breast cancer; Computer-aided annotation; Deep learning; Mitotic figures

ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings - Schmidt, Alexander; Madhu, Prathmesh; Maier, Andreas; Christlein, Vincent; Kosti, Ronak Vijaypal (2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2022)
Image enhancement algorithms are very useful for real world computer vision tasks where image resolution is often physically limited by the sensor size. While state-of-the-art deep neural networks show impressive results for image enhancement, they often struggle to enhance real-world images. In this work, we tackle a real-world setting: inpainting of images from Dunhuang caves. The Dunhuang dataset consists of murals, half of which suffer from corrosion and aging. These murals feature a range of rich content, such as Buddha statues, bodhisattvas, sponsors, architecture, dance, music, and decorative patterns designed by different artists spanning ten centuries, which makes manual restoration challenging. We modify two different existing methods (CAR, HINet) that are based upon state-of-the-art (SOTA) super resolution and deblurring networks. We show that those can successfully inpaint and enhance these deteriorated cave paintings. We further show that a novel combination of CAR and HINet, resulting in our proposed inpainting network (ARIN), is very robust to external noise, especially Gaussian noise. To this end, we present a quantitative and qualitative comparison of our proposed approach with existing SOTA networks and winners of the Dunhuang challenge. One of the proposed methods (HINet) represents the new state of the art and outperforms the 1st place of the Dunhuang Challenge, while our combination ARIN, which is robust to noise, is comparable to the 1st place. We also present and discuss qualitative results showing the impact of our method for inpainting on Dunhuang cave images.<br

A Robust Deep-Learning-based Automated Cardiac Resting Phase Detection: Validation in a Prospective Study - Yoon, Seung Su; Hoppe, Elisabeth; Schmidt, Michaela; Forman, Christoph; Chitiboi, Teodora; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Köhler, Thomas; Maier, Andreas; Hornegger, Joachim (24th International Conference on Information Processing in Medical Imaging, IPMI 2015, 2015)
Fluoroscopic images are characterized by a transparent projection of 3-D structures from all depths to 2-D. Differently moving structures, for example due to breathing and heartbeat, can be described approximately using independently moving 2-D layers. Separating the fluoroscopic images into the motion layers is desirable to facilitate interpretation and diagnosis. Given the motion of each layer, it is state of the art to compute the layer separation by minimizing a least-squares objective function. However, due to high noise levels and inaccurate motion estimates, the results are not satisfactory in X-ray images. In this work, we propose a probabilistic model for motion layer separation. In this model, we analyze various data terms and regularization terms theoretically and experimentally. We show that a robust penalty function is required in the data term to deal with noise and shortcomings of the image formation model. For the regularization term, we propose to enforce smoothness of the layers using bilateral total variation. On synthetic data, the mean squared error between the estimated layers and the ground truth is improved by 18% compared to the state of the art. In addition, we show qualitative improvements on real X-ray data.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.arobus_6

Art2Contour: Salient Contour Detection in Artworks Using Generative Adversarial Networks - Sindel, Aline; Maier, Andreas; Christlein, Vincent (2020 IEEE International Conference on Image Processing (ICIP 2020), 2020)
Artists or art workshops often reuse their motifs directly or in a slightly amended form. To allow a better comparison of these artworks, salient contours are extracted that reduce them to the most important lines or boundaries. For this task, we propose a generative adversarial network (GAN) based approach to learn the mapping from artwork images to contour drawings in a supervised manner. We introduce the combination of multiple regression task losses to encourage the learning of salient contours. For the evaluation, we created a dataset of high-resolution prints and paintings and corresponding annotated ground truth drawings. We show that our method visually and quantitatively outperforms competing methods in contour detection on prints and paintings.<br
Keywords: Contour Detection; Generative Adversarial Networks; Artworks

ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints - Sindel, Aline; Maier, Andreas; Christlein, Vincent (Computer Vision – ECCV 2022 Workshops. ECCV 2022, 2023)
Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods.
Keywords: Facial landmark detection; convolutional neural networks; artistic image synthesis; paintings; prints

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems - Amrehn, Mario; Steidl, Stefan; Kortekaas, Reinier; Strumia, Maddalena; Weingarten, Markus; Kowarschik, Markus; Maier, Andreas (International Journal of Biomedical Imaging, 2019)
For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype’s user interface (UI) features and segmentation methodologies.<br
Keywords: usability; interactive segmentation; prototype evaluation; user simulation; robot user

A Simulation Study and Experimental Verification of Hand-Eye-Calibration using Monocular X-Ray - Dorn, Petra; Fischer, Peter; Mönnich, Holger; Mewes, Philip; Khalil, Asim; Gulhar, Abhinav; Maier, Andreas (Informatik aktuell, 2018)
In this paper, the simultaneous hand-eye/robot-world problem AX = ZB is performed using a single X-ray image instead of a stereo camera in order to avoid the additional tracking device. Our setup consists of a special X-ray marker, several image preprocessing steps, and a monocular pose estimation algorithm, for extracting the 6- D pose of the marker with respect to the X-ray source. Simulations are performed to investigate the behavior of the proposed hand-eye method when including inaccuracies of the robot and the non-isotropic errors of monocular pose estimation. The simulations were evaluated in an experimental setup, reaching an accuracy of 0.06° and 0.77mm.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.asimul

A Spatiotemporal Illumination Model for 3d Image Fusion in Optical Coherence Tomography - Ploner, Stefan; Won, Jungeun; Schottenhamml, Julia; Girgis, Jessica; Lam, Kenneth; Waheed, Nadia; Fujimoto, James; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging, 2023)
Optical coherence tomography (OCT) is a non-invasive, micrometer-scale imaging modality that has become a clinical standard in ophthalmology. By raster-scanning the retina, sequential cross-sectional image slices are acquired to generate volumetric data. In-vivo imaging suffers from discontinuities between slices that show up as motion and illumination artifacts. We present a new illumination model that exploits continuity in orthogonally raster-scanned volume data. Our novel spatiotemporal parametrization adheres to illumination continuity both temporally, along the imaged slices, as well as spatially, in the transverse directions. Yet, our formulation does not make inter-slice assumptions, which could have discontinuities. This is the first optimization of a 3D inverse model in an image reconstruction context in OCT. Evaluation in 68 volumes from eyes with pathology showed reduction of illumination artifacts in 88% of the data, and only 6% showed moderate residual illumination artifacts. The method enables the use of forward-warped motion corrected data, which is more accurate, and enables supersampling and advanced 3D image reconstruction in OCT.<br

A Spatiotemporal Model for Precise and Efficient Fully-Automatic 3D Motion Correction in OCT - Ploner, Stefan; Chen, Siyu; Won, Jungeun; Husvogt, Lennart; Breininger, Katharina; Schottenhamml, Julia; Fujimoto, James; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to < 9 s, fast enough for clinical use. We present a quantitative evaluation on 18 subjects with ocular pathology and demonstrate accurate correction during microsaccades. Transverse correction is limited only by ocular tremor, whereas submicron repeatability is achieved axially (0.51 μ m median of medians), representing a dramatic improvement over previous work. This allows assessing longitudinal changes in focal retinal pathologies as a marker of disease progression or treatment response, and promises to enable multiple new capabilities such as supersampled/super-resolution volume reconstruction and analysis of pathological eye motion occurring in neurological diseases.
CRIS-Team Scopus Importer:2022-10-14
Keywords: Forward warping; Motion compensation; Non-rigid registration; Optical coherence tomography

Assessing cardiac function from total-variation-regularized 4-D C-arm CT in the presence of angular undersampling - Taubmann, Oliver; Haase, V.; Lauritsch, G.; Zheng, Y.; Krings, G.; Hornegger, Joachim; Maier, Andreas (Physics in Medicine and Biology, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.assess
Keywords: C-arm Computed Tomography; Angular Undersampling; Cardiac Function; 4-D Imaging; Total Variation; Temporal Regularization

Assessment of Handwriting in Patients With Parkinson's Disease Using Non-Intrusive Tasks - Gallo-Aristizabal, J. D.; Escobar-Grisales, D.; Rios-Urrego, C. D.; Pérez Toro, Paula Andrea; Nöth, Elmar; Maier, Andreas; Orozco Arroyave, Juan Rafael (Proceedings - International Symposium on Biomedical Imaging, 2023)
This study presents two approaches for modeling the handwriting of Parkinson's Disease (PD) patients and Healthy Control (HC) subjects. One approach is based on digit-embeddings generated from a CNN architecture pre-trained with information from the MNIST corpus. The second approach consists of the computation of statistical functionals of dynamics signal collected with the digital tablet, namely azimuth, pressure, altitude, and vertical distance. The experiments are based on writing the ten digits (from 0 to 9), which is a task commonly performed in daily life activities, making this approach closer to a non-intrusive evaluation. According to the results, the accuracy of the classification between PD patients vs. HC improved from 71.8% to 74.5% when information from images is combined with the functionals of the vertical distance signal.
CRIS-Team Scopus Importer:2023-10-06
Keywords: CNN; digital tablet; Handwriting; Parkinson's disease

Assessment of measurement deviations: length-extended x-ray imaging for orthopedic applications - Luckner, Christoph; Magdalena, Herbst; Ritschl, Ludwig; Maier, Andreas; Kappler, Steffen (Medical Imaging 2019: Physics of Medical Imaging, 2019)

Assessment of segmentation dependence in macroscopic lung cavity extraction - Khdeir, Asmaa; Geimer, Tobias; Chen, Shuqing; Goppert, Eric; Dankbar, Maximilian; Bert, Christoph; Maier, Andreas (Informatik aktuell, 2018)
Training of respiratory motion models and population-based patient phantoms of the lung often requires the definition of the entire lung cavity region in the 4D-CT. To ease the workload of clinical experts, automatic selection is highly desirable. Many lung cavity extraction methods rely on a pre-segmented lung volume.
CRIS-Team Scopus Importer:2022-06-05

Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval - Bootwala, Azeem; Breininger, Katharina; Maier, Andreas; Christlein, Vincent (Bildverarbeitung für die Medizin 2020, 2020)
Image-based diagnosis of the human eye is crucial for the early detection of several diseases in ophthalmology. In this work, we investigate the possibility to use image retrieval to support the diagnosis of diabetic retinopathy. To this end, we evaluate different feature learning techniques. In particular, we evaluate the performance of cost functions specialized for metric learning, namely, contrastive loss, triplet loss and histogram loss, and compare them with the classification crossentropy loss. Additionally, we train the network on images graded by diabetic retinopathy severity and transfer the knowledge learned, to retrieve images that are graded by diabetic macular edema severity and evaluate our algorithm on three different datasets. For the task of detecting referable/non-referable diabetic retinopathy, we achieve a sensitivity of 0.84 and specificity of 0.88 on the Kaggle dataset using histogram loss. On the Messidor dataset, we achieve a sensitivity and specificity score of 0.79 and 0.84, respectively.<br

A study on the accuracy of polynomial fitting for direct computation of material lengths from projection data in dual energy CT - Haase, V.; Stierstorfer, K.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2023)
Dual energy (DE) CT is on the path to dramatically improve the impact of CT on patient healthcare. We are particularly interested in accurate estimation of tissue attenuation values, a key potential of DECT yet to be achieved. In many implementations of DECT, the two scans can be decomposed in the projection domain before image reconstruction. This decomposition amounts to solving a pair of non-linear equations for each measurement line. A computationally efficient approach addressing this problem is to directly express the solution with a polynomial fit. Most often, a cubic order is deemed sufficient, and the polynomial coefficients are found with calibration scans through combinations of positive lengths of the basis materials. Alternatively, when the energy response of the measurements is known, calibration scans are not needed, and both negative and positive material lengths can be considered. Negative material lengths can be useful to account for quantum noise and partial volume effects, and to enrich the set of tissues that can be exactly represented by the basis materials. In this work, we study the accuracy of the polynomial fitting assuming that the measurements have been corrected for scatter and the energy responses are known, using a data processing pipeline we recently published. Experimental results based on CT scans of the ACR phantom show that negative lengths are not uncommon, that fitting based on positive lengths therefore inherently involve unsafe extrapolation, and that quintic polynomial fitting with negative and positive material lengths yield robust and accurate results.
CRIS-Team Scopus Importer:2023-06-09
Keywords: ACR CT accreditation phantom; analytical energy response model; Dual energy CT; material decomposition; polynomial fitting

A Survey of Sensors in Healthcare Workflow Monitoring - Stoffel Antunes, Rodolfo; Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Facco Rodrigues, Vinicius; Andre da Costa, Cristiano; da Silveira Junior, Luiz Gonzaga; da Rosa Righi, Rodrigo; Maier, Andreas; Eskofier, Björn; Ollenschläger, Malte; Naderi Boldaji, Farzad; Fahrig, Rebecca; et al. (ACM Computing Surveys, 2018)
Keywords: Real-time location systems; Workflow monitoring; Computer vision; Healthcare

Atlas-Based Linear Volume-of-Interest (ABL-VOI) Image Correction - Maier, Andreas; Jiang, Zhenzhen; Jordan, Johannes Michael; Riess, Christian; Hofmann, Hannes; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)

Attention-guided Erasing - Bhandary Panambur, Adarsh; Yu, Hui; Bhat, Sheethal; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed attention-guided erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.<br

Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification - Kulyabin, Mikhail; Constable, Paul; Zhdanov, Aleksei; Lee, Irene; Thompson, Dorothy A.; Maier, Andreas (IEEE Access, 2024)
The electroretinogram (ERG) is a clinical test that records the retina’s electrical response to a brief flash of light as a waveform signal. Analysis of the ERG signal offers a promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by poor communication, reduced reciprocal social interaction, and restricted and repetitive stereotyped behaviors that should be detected as early as possible to ensure timely and appropriate intervention to support the individual and their family. In this study, we applied gated Multilayer Perceptron (gMLP) for the light-adapted ERG waveform classification as an effective alternative to Transformers. This study presents the first application of gMLP for ASD classification, which employs basic multilayer perceptrons with fewer parameters than Transformers. We compared the performance of different time-series models on an ASD-Control dataset and found that the superiority of gMLP in classification accuracy was the best at 89.7% compared to alternative models and supports the use of gMLP in classification models based on ERG recordings involving case-control comparison
Keywords: ASD; deep learning; electroretinogram; ERG; gated MLP; transformer; waveform

AUCReshaping: improved sensitivity at high-specificity - Bhat, Sheethal; Mansoor, Awais; Georgescu, Bogdan; Bhandary Panambur, Adarsh; Ghesu, Florin C.; Islam, Saahil; Packhäuser, Kai; Rodriguez Salas, Dalia; Grbic, Sasa; Maier, Andreas (Scientific Reports, 2023)
The evaluation of deep-learning (DL) systems typically relies on the Area under the Receiver-Operating-Curve (AU-ROC) as a performance metric. However, AU-ROC, in its holistic form, does not sufficiently consider performance within specific ranges of sensitivity and specificity, which are critical for the intended operational context of the system. Consequently, two systems with identical AU-ROC values can exhibit significantly divergent real-world performance. This issue is particularly pronounced in the context of anomaly detection tasks, a commonly employed application of DL systems across various research domains, including medical imaging, industrial automation, manufacturing, cyber security, fraud detection, and drug research, among others. The challenge arises from the heavy class imbalance in training datasets, with the abnormality class often incurring a considerably higher misclassification cost compared to the normal class. Traditional DL systems address this by adjusting the weighting of the cost function or optimizing for specific points along the ROC curve. While these approaches yield reasonable results in many cases, they do not actively seek to maximize performance for the desired operating point. In this study, we introduce a novel technique known as AUCReshaping, designed to reshape the ROC curve exclusively within the specified sensitivity and specificity range, by optimizing sensitivity at a predetermined specificity level. This reshaping is achieved through an adaptive and iterative boosting mechanism that allows the network to focus on pertinent samples during the learning process. We primarily investigated the impact of AUCReshaping in the context of abnormality detection tasks, specifically in Chest X-Ray (CXR) analysis, followed by breast mammogram and credit card fraud detection tasks. The results reveal a substantial improvement, ranging from 2 to 40%, in sensitivity at high-specificity levels for binary classification tasks.<br
Keywords: Electrical and electronic engineering; Engineering

Audio-Visual Feedback System for Reward-Based Training Sessions of Elderly People in a Home Environment - Soutschek, Stefan; Maier, Andreas; Hönig, Florian Thomas; Spiegl, Werner; Steidl, Stefan; Hornegger, Joachim; Erzigkeit, Helmut; Kornhuber, Johannes (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Augmented Mitotic Cell Count Using Field of Interest Proposal - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Informatik aktuell, 2019)
Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.
CRIS-Team Scopus Importer:2019-05-14

Augmented reality-based feedback for technician-in-the-loop C-arm repositioning - Unberath, Mathias; Fotouhi, Javad; Hajek, Jonas; Maier, Andreas; Osgood, Greg; Taylor, Russell; Armand, Mehran; Navab, Nassir (Healthcare Technology Letters, 2018)
Interventional C-arm imaging is crucial to percutaneous orthopedic procedures as it enables the surgeon to monitor the progress of surgery on the anatomy level. Minimally invasive interventions require repeated acquisition of X-ray images from different anatomical views to verify tool placement. Achieving and reproducing these views often comes at the cost of increased surgical time and radiation. We propose a marker-free ‘technician-in-the-loop’ Augmented Reality (AR) solution for C-arm repositioning. The X-ray technician operating the C-arm interventionally is equipped with a head-mounted display system capable of recording desired C-arm poses in 3D via an integrated infrared sensor. For C-arm repositioning to a target view, the recorded pose is restored as a virtual object and visualized in an AR environment, serving as a perceptual reference for the technician. Our proof-of-principle findings from a simulated trauma surgery indicate that the proposed system can decrease the 2.76 X-ray images required for re-aligning the scanner with an intra-operatively recorded C-arm view down to zero, suggesting substantial reductions of radiation dose. The proposed AR solution is a first step towards facilitating communication between the surgeon and the surgical staff, improving the quality of surgical image acquisition, and enabling context-aware guidance for surgery rooms of the future.
CRIS-Team Scopus Importer:2022-06-05

A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging - Köhler, Thomas; Jordan, Johannes Michael; Maier, Andreas; Hornegger, Joachim (Proceedings of the British Machine Vision Conference (BMVC), 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.aunifi

Author Correction: DeepNAPSI multi-reader nail psoriasis prediction using deep learning. - Folle, Lukas; Fenzl, Pauline; Fagni, Filippo; Thies, Mareike; Christlein, Vincent; Meder, Christine; Simon, David; Minopoulou, Ioanna; Sticherling, Michael; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Scientific Reports, 2023)
EVALuna2:538275

Autoblog 2021: The Importance of Language Models for Spontaneous Lecture Speech - Hernandez, Abner; Klumpp, Philipp; Das, Badhan Kumar; Maier, Andreas; Yang, Seung Hee (Text, Speech, and Dialogue 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings, 2022)
The demand for both quantity and quality of online educational resources has skyrocketed during the last two years’ pandemic. Entire course series had since been recorded and distributed online. To reach a broader audience, videos could be transcribed, combined with supplementary material (e.g. lecture slides) and published in the style of blog posts. This had been done previously for Autoblog 2020, a corpus of lecture recordings that had been converted to blog posts, using automated speech recognition (ASR) for subtitle creation. This work aims to introduce a second series of recorded and manually transcribed lecture videos. The corresponding data includes lecture videos, slides, and blog posts/transcripts with aligned slide images and is published under creative commons license. A state-of-the-art Wav2Vec ASR model was used for automatic transcription of the content, using different n-gram language models (LM). The results were compared to the human ground truth annotation. Findings indicated that the ASR performed well on spontaneous lecture speech. Furthermore, LMs trained on large amounts of data with fewer out-of-vocabulary words were outperformed by much smaller LMs estimated over in-domain language. Annotated lecture recordings were deemed helpful for the creation of task-specific ASR solutions as well as their validation against a human ground truth.<br
Keywords: Lecture video corpus; Speech recognition; Language model; Spontaneous lecture speech

Automated Billing Code Prediction from MRI Log Data - Denck, Jonas; Landschuetz, Wilfried; Nairz, Knud; Heverhagen, Johannes T.; Maier, Andreas; Rothgang, Eva (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition), 2018)

Automated Billing Code Retrieval from MRI Scanner Log Data - Denck, Jonas; Landschütz, Wilfried; Nairz, Knud; Heverhagen, Johannes T.; Maier, Andreas; Rothgang, Eva (Journal of Digital Imaging, 2019)
Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.
CRIS-Team Scopus Importer:2019-07-09
Keywords: Machine learning; Magnetic resonance imaging; Medical coding; Reimbursement; Workflow enhancement

Automated Bone Density Measurements using Deep Learning - Folle, Lukas; Meinderink, Timo; Simon, David; Liphardt, Anna-Maria; Thies, Mareike; Krönke, Gerhard; Schett, Georg; Kleyer, Arnd; Maier, Andreas (23, 2022)
Introduction Hand bone loss is characteristic of rheumatoid or psoriatic arthritis, which clinically manifests with pain, swelling, and stiffness of the affected joints. Typically, hand joints are affected already in the early stages of arthritis1. High-resolution peripheral quantitative computed tomography (HR-pQCT) allows to visualize structural changes in the bone in 3D and enables measurements derived from the scans of the patient’s hand such as the volume of a bone. For this, a segmentation of the scans is necessary, which is reader-dependent and time-consuming, especially in challenging cases2. This work aims to replace this manual expert segmentation with a neural network (figure 1). Materials and methods The segmentation network was trained using 541 HR-pQCT patient scans (RA, PsA, and OA patients with motion grades up to 3) as input and the corresponding expert segmentation of the second metacarpal bone as reference. To optimize the parameters of the network, the current prediction was compared with the expert segmentation using the Dice coefficient. Pre-processing steps included resizing, flipping of left hands, and intensity normalization. The architecture of the segmentation network is based on the 2D U-Net, which was pre-trained on ImageNet. The predictions were resized to the original resolution and converted to the scanner-specific data format as post-processing steps. The scanner then calculated the average bone-mineral density (D100) based on the scan and the corresponding network-generated segmentation. The scans were split into training, validation, and testing with ratios of 70%, 20%, and 10% respectively. Results and discussion The bone segmentation using the neural network on the test set reached a Dice coefficient of 97.2%. A quantitative comparison of the network prediction (blue) and the expert annotation (red) is presented in figure 2. The deviation of the expert annotation from the bone contour is likely due to the shift of the scan when exceeding the specified maximum number of slices of the scanner. Since the neural network operates on a slice level, such deviations are implicitly accounted for. The results of the vBMD analysis are summarized in figure 3. Significant Pearson correlation with 0.999 (p<.001) was achieved for the D100 on the test set. Still, a slight offset is present in the predictions, which might lead to underestimating the D100. However, this could be eliminated in a calibration step beforehand. Conclusion Neural networks can be used to generate accurate segmentations of the MCP2 head even with erosions present and can be integrated into the clinical workflow with a performance on par with expert annotators and save time through the automation of the annotation process. <
Keywords: deep learning; bone imaging; segmentation

Automated Calculation of Alberta Stroke Program Early CT Score Validation in Patients With Large Hemispheric Infarct - Albers, Gregory W.; Wald, Michael J.; Mlynash, Michael; Endres, Juergen; Bammer, Roland; Straka, Matus; Maier, Andreas; Hinson, Holly E.; Sheth, Kevin N.; Kimberly, W. Taylor; Molyneaux, Bradley J. (Stroke, 2019)
Background and Purpose- We compared the Alberta Stroke Program Early CT Score (ASPECTS), calculated using a machine learning-based automatic software tool, RAPID ASPECTS, as well as the median score from 4 experienced readers, with the diffusion-weighted imaging (DWI) ASPECTS obtained following the baseline computed tomography (CT) in patients with large hemispheric infarcts. Methods- CT and magnetic resonance imaging scans from the GAMES-RP study, which enrolled patients with large hemispheric infarctions (82-300 mL) documented on DWI-magnetic resonance imaging, were evaluated by blinded experienced readers to determine both CT and DWI ASPECTS. The CT scans were also evaluated by an automated software program (RAPID ASPECTS). Using the DWI ASPECTS as a reference standard, the median CT ASPECTS of the clinicians and the automated score were compared using the interclass correlation coefficient. Results- The median CT ASPECTS for the clinicians was 5 (interquartile range, 4-7), for RAPID ASPECTS 3 (interquartile range, 1-6), and for DWI ASPECTS 3 (2-4). Median error for RAPID ASPECTS was 1 (interquartile range, -1 to 3) versus 3 (interquartile range, 1-4) for clinicians (P<0.001). The automated score had a higher level of agreement with the median of the DWI ASPECTS, both for the full scale and when dichotomized at <6 versus 6 or more (difference in intraclass correlation coefficient, P=0.001). Conclusions- RAPID ASPECTS was more accurate than experienced clinicians in identifying early evidence of brain ischemia as documented by DWI.
CRIS-Team WoS Importer:2019-11-12

Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery - Yoon, Seung Su; Preuhs, Elisabeth; Schmidt, Michaela; Forman, Christoph; Chitiboi, Teodora; Sharma, Puneet; Fernandes, Juliano L.; Tillmanns, Christoph; Wetzl, Jens; Maier, Andreas (Journal Machine Learning for Biomedical Imaging, 2023)

Automated Coronary Artery Ostia Detection in Magnetic Resonance Angiography - Stimpel, Bernhard; Forman, Christoph; Wetzl, Jens; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_3

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography - Stimpel, Bernhard; Wetzl, Jens; Forman, Christoph; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Journal of Imaging, 2018)
Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24±5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76±0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1±0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.automa_94
Keywords: Computer-aided detection and diagnosis; Coronary artery; Magnetic resonance angiography; Planar reformation

Automated Deep-Learning-based Inversion Time Selection for Cardiac Late Gadolinium Enhancement Imaging - Yoon, Seung Su; Schmidt, Michaela; Wintersperger, Bernd J.; Chitiboi, Teodora; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests. - Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian; Bai, Wenja; Rueckert, Daniel; Maier, Andreas (Journal of Medical Engineering, 2017)
The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
Keywords: MRI; bulk head motion; respiratory motion; k-space; sampling trajectories; random decision forests

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests - Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian; Bai, Wenjia; Rueckert, Daniel; Maier, Andreas (Journal of Medical Engineering, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.automa_8
Keywords: image reconstruction; artifacts; classification

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images - Fragoso-Garcia, Marco; Wilm, Frauke; Bertram, Christof A.; Merz, Sophie; Schmidt, Anja; Donovan, Taryn; Fuchs-Baumgartinger, Andrea; Bartel, Alexander; Marzahl, Christian; Diehl, Laura; Puget, Chloe; Maier, Andreas; et al. (Veterinary Pathology, 2023)
Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.
CRIS-Team Scopus Importer:2023-08-11
Keywords: computational pathology; computer-aided diagnosis; digital pathology; dog; machine learning; skin; veterinary oncology

Automated Left Ventricle Segmentation in 2-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_2
Keywords: Heart; Image Segmentation; Magnetic Resonance Imaging (MRI)

Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (STACOM 2019, 2019)
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject to inter- and intra-observer variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multi-sequence CMR segmentation task using transfer learning that combines multi-source image information. We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features. The domain-adapted network was trained with just four LGE-MR training samples and obtained an average Dice score of 85.0% on the test set comprises of 40 LGE-MR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGE-MR training data. <br

Automated postlaryngectomy telephone test - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (HNO, 2009)
Objective: In this study, an objective version of the postlaryngectomy telephone test (PLTT) for measuring speech intelligibility based on automatic speech recognition is presented. Methods: Thirty-one patients with tracheoesophageal substitute voice (25 men and six women, 63.4±8.7 years) were evaluated by 11 naïve listeners. The automatic measurement of speech intelligibility was expressed by means of word accuracy and word recognition rates, or the percentage of correctly recognized words from a word sequence. These automatic measures were compared with the subjectively obtained PLTT values. Results: The average PLTT intelligibility of the 11 naïve listeners was 47%; the automatically obtained word accuracy and word recognition rates were much lower (approximately 0% and 15%, respectively). The correlation between subjective and automatic evaluation, however, reached more than 0.9 in some of the examined cases. Conclusion: Automatic speech recognition provides an efficient, objective measure that is equivalent to the overall PLTT intelligibility value. © 2009 Springer Medizin Verlag.
Keywords: Automated pattern recognition; Correlation of data; Evaluation methodology; Speech intelligibility; Speech recognition software

Automated Protocoling for MRI Exams-Challenges and Solutions - Denck, Jonas; Haas, Oliver; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Journal of Digital Imaging, 2022)
Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.
CRIS-Team WoS Importer:2022-09-09

Automated segmentation of 3D cine cardiovascular magnetic resonance imaging - Tayebi Arasteh, Soroosh; Romanowicz, Jennifer; Pace, Danielle F.; Golland, Polina; Powell, Andrew J.; Maier, Andreas; Truhn, Daniel; Brosch, Tom; Weese, Juergen; Lotfinia, Mahshad; van der Geest, Rob J.; Moghari, Mehdi H. (Frontiers in Cardiovascular Medicine, 2023)
Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.
Keywords: congenital heart disease; deep learning; 3D cine; CMR image analysis; automatic segmentation

Automated Time Adjusted Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging – Validation and Application on Patient Datasets - Yoon, Seung Su; Schmidt, Michaela; Rick, Manuela; Chitiboi, Teodora; Sharma, Puneet; Emrich, Tilman; Tillmanns, Christoph; Seitz, Andreas; Mahrholdt, Heiko; Toupin, Solenn; Pezel, Théo; Garot, Jérome; et al. (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Automatically evaluated degree of intelligibility of children with different cleft type from preschool and elementary school measured by automatic speech recognition - Schuster, Maria; Maier, Andreas; Bocklet, Tobias; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Stelzle, Florian (International Journal of Pediatric Otorhinolaryngology, 2012)

Automatic Assessment of Alzheimer's across Three Languages Using Speech and Language Features - Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Braun, F.; Hönig, F.; Tobón-Quintero, C. A.; Aguillón, D.; Lopera, F.; Hincapié-Henao, L.; Schuster, M.; Riedhammer, K.; Maier, Andreas; Nöth, Elmar; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
With the increasing prevalence of Alzheimer's Disease (AD) worldwide, it is essential to develop non-invasive methods to monitor the progression of the disease. Speech and language analyses are suitable for detecting the cognitive impairment of AD patients; thus, by analyzing changes in speech patterns and language use, researchers can develop methods to monitor AD remotely. In this paper, we investigated several speech and language techniques commonly used for the automatic detection of AD. Furthermore, we considered speech recordings of 448 patients in three different languages: Spanish (57), German (205), and English (186). Cross-lingual analysis was carried out using two classification approaches: (1) training/testing in one or more languages and (2) training in one language and testing in another. We obtained unweighted average recall values of up to 83% to classify AD using the first classification approach and up to 70% with the second.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Alzheimer's Disease; Cross-lingual Analysis; Deep Learning; Dementia; Pathological Speech Processing

Automatic Assessment of Children's Speech with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Nkenke, Emeka; Schuster, Maria (Language Technologies, IS-LTC 2006, 2006)

Automatic CAD-RADS Scoring using Deep Learning - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design – which allows comprehensive predictions.<br
Keywords: Coronary Artery Disease; Coronary CT Angiography; Deep Learning; Data Representation; CAD-RADS

Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks - Yoon, Seung Su; Hoppe, Elisabeth; Schmidt, Michaela; Forman, Christoph; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)

Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Automatic Chain Line Segmentation in Historical Prints - Biendl, Meike; Sindel, Aline; Klinke, Thomas; Maier, Andreas; Christlein, Vincent (Pattern Recognition. ICPR International Workshops and Challenges, 2021)
The analysis of chain line patterns in historical prints can provide valuable information about the origin of the paper. For this task, we propose a method to automatically detect chain lines in transmitted light images of prints from the 16th century. As motifs and writing on the paper partially occlude the paper structure, we utilize a convolutional neural network in combination with further postprocessing steps to segment and parametrize the chain lines. We compare the number of parametrized lines, as well as the distances between them, with reference lines and values. Our proposed method is an effective method showing a low error of less than 1 mm in comparison to the manually measured chain line distances.<br
Keywords: Line segmentation; Line detection; Chain lines; Historical prints; Paper structure

Automatic Classification and Pathological Staging of Confocal Laser Endomicroscopic Images of the Vocal Cords - Vo, Kim; Jaremenko, Christian; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen, 2017)
Confocal laser endomicroscopy is a novel imaging technique which provides real-time in vivo examination and histological analysis of tissue during an ongoing endoscopy. We present an automatic classification system that is able to differentiate between healthy and cancerous tissue of the vocal cords. Textural as well as CNN features are encoded using Fisher vectors and vector of locally aggregated descriptors while the classification is performed using random forests and support vector machines. Two experiments are investigated following a leave-one-sequence-out cross-validation and a fixed training and test set approach. Classification rates reach up to 87.6 % and 81.5 %, respectively.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_7

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning - Aubreville, Marc; Knipfer, Christian; Oetter, Nicolai; Jaremenko, Christian; Rodner, Erik; Denzler, Joachim; Bohr, Christopher; Neumann, Helmut; Stelzle, Florian; Maier, Andreas (Scientific Reports, 2017)
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, su cient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 speci c locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, speci city 90%).
Keywords: Cancer imaging; translational research; deep learning

Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images - Deitsch, Sergiu; Christlein, Vincent; Berger, Stephan; Buerhop-Lutz, Claudia; Maier, Andreas; Gallwitz, Florian; Riess, Christian (Solar Energy, 2019)
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.<br
CRIS-Team Scopus Importer:2019-05-09
Keywords: Deep learning; defect classification; electroluminescence imaging; photovoltaic modules; regression analysis; support vector machines; visual inspection

Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging - Schlereth, Maja; Stromer, Daniel; Breininger, Katharina; Wagner, Alexandra; Tan, Lina; Maier, Andreas; Knieling, Ferdinand (Informatik aktuell, 2022)
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne muscular dystropy (DMD) and spinal muscular atrophy (SMA), are difficult to differentiate at the beginning and worsen quickly, fast and reliable differential diagnosis is crucial. Photoacoustic and ultrasound imaging has shown great potential to visualize and quantify the extent of different diseases. The addition of automatic classification of such image data could further improve standard diagnostic procedures.We compare deep learning-based 2-class and 3-class classifiers based on VGG16 for differentiating healthy from diseased muscular tissue. This work shows promising results with high accuracies above 0.86 for the 3-class problem and can be used as a proof of concept for future approaches for earlier diagnosis and therapeutic monitoring of NMDs.<br
CRIS-Team Scopus Importer:2022-05-06