VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
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arXiv
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| Format: | Preprint |
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2026
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| author | Yazıcı, Ziya Ata Gezer, N. Sinem Öksüz, İlkay Koska, İlker Özgür Toprak, Tuğçe Bulucu, Pervin Beşenk, Ufuk Kavur, A. Emre Conze, Pierre-Henri Ekenel, Hazım Kemal Dicle, Oğuz Şeker, Mustafa Ege Kartal, Mustafa Said Moniri, Ariorad Özkan, Orhan Bayram, Osman Faruk Polat, Hakan Balcı, Musa Cebeci, Ece Tuğba Cılga, Baran Peçenek, Kardelen Selver, M. Alper |
| author_facet | Yazıcı, Ziya Ata Gezer, N. Sinem Öksüz, İlkay Koska, İlker Özgür Toprak, Tuğçe Bulucu, Pervin Beşenk, Ufuk Kavur, A. Emre Conze, Pierre-Henri Ekenel, Hazım Kemal Dicle, Oğuz Şeker, Mustafa Ege Kartal, Mustafa Said Moniri, Ariorad Özkan, Orhan Bayram, Osman Faruk Polat, Hakan Balcı, Musa Cebeci, Ece Tuğba Cılga, Baran Peçenek, Kardelen Selver, M. Alper |
| contents | Accurate segmentation of hepatic and portal vessels in contrast-enhanced computed tomography angiography (CTA) remains challenging due to complex vascular topology, peripheral visibility limitations, and acquisition-induced ambiguities. While existing public datasets offer valuable benchmarks, few include clinically realistic annotation constraints. We introduce VEELA (Vessel Extraction and Extrication for Liver Analysis), a rigorously curated liver vessel dataset derived from 40 CTA scans inherited from the CHAOS grand-challenge cohort. All vessels were manually delineated slice-by-slice under multi-expert consensus, using a strict visibility-driven annotation policy and avoiding anatomically inferred interpolation. This design explicitly captures anatomical variability and imaging-related uncertainty. As a continuation of the CHAOS challenge, VEELA enables reproducible cross-benchmark evaluation while extending the scope to fine-grained hepatic and portal vessel segmentation. We further establish a standardized benchmarking framework and analyze complementary evaluation metrics, including topology-aware (clDice), overlap-based (IoU), boundary-sensitive (NSD), and geometry-aware (area, length) measures. Our results demonstrate that different metrics capture distinct aspects of vascular integrity, underscoring the necessity of multi-perspective evaluation for clinically meaningful vessel segmentation. VEELA is publicly released to facilitate reproducible research and support the development of robust vascular segmentation methods. Researchers can access the evaluation metrics, dataset, and submission platform at https://www.synapse.org/Synapse:syn65471967. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22357 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography Yazıcı, Ziya Ata Gezer, N. Sinem Öksüz, İlkay Koska, İlker Özgür Toprak, Tuğçe Bulucu, Pervin Beşenk, Ufuk Kavur, A. Emre Conze, Pierre-Henri Ekenel, Hazım Kemal Dicle, Oğuz Şeker, Mustafa Ege Kartal, Mustafa Said Moniri, Ariorad Özkan, Orhan Bayram, Osman Faruk Polat, Hakan Balcı, Musa Cebeci, Ece Tuğba Cılga, Baran Peçenek, Kardelen Selver, M. Alper Computer Vision and Pattern Recognition Artificial Intelligence Accurate segmentation of hepatic and portal vessels in contrast-enhanced computed tomography angiography (CTA) remains challenging due to complex vascular topology, peripheral visibility limitations, and acquisition-induced ambiguities. While existing public datasets offer valuable benchmarks, few include clinically realistic annotation constraints. We introduce VEELA (Vessel Extraction and Extrication for Liver Analysis), a rigorously curated liver vessel dataset derived from 40 CTA scans inherited from the CHAOS grand-challenge cohort. All vessels were manually delineated slice-by-slice under multi-expert consensus, using a strict visibility-driven annotation policy and avoiding anatomically inferred interpolation. This design explicitly captures anatomical variability and imaging-related uncertainty. As a continuation of the CHAOS challenge, VEELA enables reproducible cross-benchmark evaluation while extending the scope to fine-grained hepatic and portal vessel segmentation. We further establish a standardized benchmarking framework and analyze complementary evaluation metrics, including topology-aware (clDice), overlap-based (IoU), boundary-sensitive (NSD), and geometry-aware (area, length) measures. Our results demonstrate that different metrics capture distinct aspects of vascular integrity, underscoring the necessity of multi-perspective evaluation for clinically meaningful vessel segmentation. VEELA is publicly released to facilitate reproducible research and support the development of robust vascular segmentation methods. Researchers can access the evaluation metrics, dataset, and submission platform at https://www.synapse.org/Synapse:syn65471967. |
| title | VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.22357 |