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Veela Challenge - Vessel Extraction and Extrication for Liver Analysis

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Abstract

Precise segmentation of liver vasculature remains a critical yet challenging objective in clinical procedures, owing to anatomical complexity, inter-patient variability, and inherent radiological artifacts. We introduce VEELA (Vessel Extraction and Extrication for Liver Analysis) challenge, which presents a dataset of 40 abdominal CTA scans from liver transplant donors, derived from the CHAOS challenge. VEELA features comprehensive annotations of hepatic and portal veins, including peripheral vessels. In this paper, the performance of baseline models and the top three performing submissions are provided. The dataset and trained models are publicly available to advance liver vessel segmentation research through VEELA Synapse website.

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[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Segmentation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Liver vascular tree, Deep learning, Classification, Computed tomography

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