141 citations · 332 across the 67 of their papers we have counts for
25 papers · 1 filter
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Robert Graf, Tanja Lerchl, Kati Nispel +7
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical mo…
Topology Optimization in Medical Image Segmentation with Fast Euler Characteristic
Liu Li, Qiang Ma, Cheng Ouyang +3
Deep learning-based medical image segmentation techniques have shown promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Unio…
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold +2
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for pr…
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Alexander Weers, Alexander H. Berger, Laurin Lux +3
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show p…
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation
Ying Chen, Rami Al-Maskari, Izabela Horvath +14
Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Com…
TG-OT: Topology-guided CCTA-IVUS registration via optimal transport matching
R. L. M. van Herten, José P. Henriques, R. Nils Planken +5
Registering coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) enables comprehensive coronary analysis that neither modality can provide alone, yet their fusion rem…
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel +17
Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the…
Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
Bastian Wittmann, Lukas Glandorf, Johannes C. Paetzold +4
Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzhe…
Topologically Faithful Multi-class Segmentation in Medical Images
Alexander H. Berger, Nico Stucki, Laurin Lux +6
Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting…
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
Chinmay Prabhakar, Hongwei Bran Li, Johannes C. Paetzold +6
Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is cru…
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Linus Kreitner, Johannes C. Paetzold, Nikolaus Rauch +11
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocul…
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Florian Kofler, Felix Meissen, Felix Steinbauer +103
A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Robbie Holland, Oliver Leingang, Christopher Holmes +13
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into…
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit +7
Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g.,…
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
Linus Kreitner, Ivan Ezhov, Daniel Rueckert +2
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a n…
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Felix Meissen, Johannes Paetzold, Georgios Kaissis +1
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the ano…
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Martin J. Menten, Johannes C. Paetzold, Alina Dima +3
Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to…
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Lucas Fidon, Suprosanna Shit, Ivan Ezhov +3
Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizabili…
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Izabela Horvath, Johannes C. Paetzold, Oliver Schoppe +6
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research. Yet, a massive lack of annotations prevents the broad…
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Florian Kofler, Ivan Ezhov, Fabian Isensee +16
Metrics optimized in complex machine learning tasks are often selected in an ad-hoc manner. It is unknown how they align with human expert perception. We explore the correlations b…
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
Kelly Payette, Priscille de Dumast, Hamza Kebiri +17
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.…
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Stefan Gerl, Johannes C. Paetzold, Hailong He +7
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis. However, both th…
Inferring the 3D Standing Spine Posture from 2D Radiographs
Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold +5
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural…