194 citations
- KU LeuvenBE11 papers
- Technical University of MunichDE8 papers
- InsermFR4 papers
- TUM KlinikumDE4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Universitair Ziekenhuis LeuvenBE3 papers
- Universitat de GironaES3 papers
- University of BernCH3 papers
- University of ZurichCH3 papers
- Centre de Recherche en Acquisition et Traitement de l'Image pour la SantéFR2 papers
- Harvard UniversityUS2 papers
- Helmholtz MunichDE2 papers
16 papers
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge
Santiago Gómez, Daniel Mantilla, Gustavo Garzón +4
Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in local…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
Jeroen Bertels, David Robben, Dirk Vandermeulen +1
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the simi…
Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions
Luca Canalini, Jan Klein, Nuno Pedrosa de Barros +3
In this work, we compare five deep learning solutions to automatically segment the resection cavity in postoperative MRI. The proposed methods are based on the same 3D U-Net archit…
The Dice loss in the context of missing or empty labels: Introducing and
Sofie Tilborghs, Jeroen Bertels, David Robben +2
Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimizati…