From the 1 of 17 linked papers with an AI index.
12 citations · 12 across the 1 of their papers we have counts for
17 papers
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…
Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation
Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5
Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV),…
Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Tassilo Wald, Saikat Roy, Fabian Isensee +7
Transformers have achieved remarkable success across multiple fields, yet their impact on 3D medical image segmentation remains limited with convolutional networks still dominating…
nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
Alexandra Ertl, Stefan Denner, Robin Peretzke +8
Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Yudi Sang, Yanzhen Liu, Sutuke Yibulayimu +33
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and effici…