390 citations · 1.1k across the 61 of their papers we have counts for
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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…
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
David Lüdke, Tamaz Amiranashvili, Felix Ambellan +3
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shap…
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Moritz Roman Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning +22
Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous…
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
Florian Kofler, Ivan Ezhov, Lucas Fidon +14
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We eva…
blob loss: instance imbalance aware loss functions for semantic segmentation
Florian Kofler, Suprosanna Shit, Ivan Ezhov +14
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volum…
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
Ivan Ezhov, Marcel Rosier, Lucas Zimmer +12
Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical them…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Relationformer: A Unified Framework for Image-to-Graph Generation
Suprosanna Shit, Rajat Koner, Bastian Wittmann +8
A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blo…
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Fernando Navarro, Guido Sasahara, Suprosanna Shit +4
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For in…