17 citations · 54 across the 8 of their papers we have counts for
8 papers
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…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Future Unruptured Intracranial Aneurysm Growth Prediction using Mesh Convolutional Neural Networks
Kimberley M. Timmins, Maarten J. Kamphuis, Iris N. Vos +3
The growth of unruptured intracranial aneurysms (UIAs) is a predictor of rupture. Therefore, for further imaging surveillance and treatment planning, it is important to be able to…
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…
Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning
V. de Vos, K. M. Timmins, I. C. van der Schaaf +3
Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magn…
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs
Kimberley M. Timmins, Irene C. van der Schaaf, Ynte M. Ruigrok +2
Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) enable visualization and analysis of cerebral arteries. This analysis may indicate normal variation of the configuration of…