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From the 1 of 22 linked papers with an AI index.

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20242026
most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 12 across the 5 of their papers we have counts for

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22 papers

cs.CV202612 cited

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…

cs.LG2026

Routine laboratory trajectories encode the onset of organ-level complications in cancer

Jannik Lübberstedt, Krischan Braitsch, Jacqueline Lammert +21

Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-time…

cs.CV2026

Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation

Erik Großkopf, Soumya Snigdha Kundu, Hendrik Möller +9

The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between…

cs.CV2026

Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering

Christian Wachinger, Bernhard Renger, Christopher Späth +2

Interpreting quantitative CT biomarkers, such as organ volume and tissue attenuation, requires large-scale healthy reference distributions. However, creating these is challenging b…

cs.CV2026

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation

Nathan Molinier, Hendrik Möller, Thomas Dagonneau +6

Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. However, model performance remains limited b…

cs.CV2026

Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT

Matan Atad, Alexander W. Marka, Lisa Steinhelfer +10

Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often r…