Veela Challenge - Vessel Extraction and Extrication for Liver Analysis

Tuǧçe Toprak; Ziya Ata Yazici; Ilkay Öksüz; Ilker Özgür Koska; Pervin Bulucu; N. Sinem Gezer; Ufuk Beşenk; A. Emre Kavur; Pierre Henri Conze; Hazim Kemal Ekenel; Oǧuz Dicle; M. Alper Selver

doi:10.1109/MLSP62443.2025.11204261

Veela Challenge - Vessel Extraction and Extrication for Liver Analysis

Tuǧçe Toprak^*, Ziya Ata Yazici, Ilkay Öksüz^*, Ilker Özgür Koska^*, Pervin Bulucu^*, N. Sinem Gezer, Ufuk Beşenk^*, A. Emre Kavur^*, Pierre Henri Conze, Hazim Kemal Ekenel, Oǧuz Dicle, M. Alper Selver

^*Corresponding author for this work

Department of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.1https://www.synapse.org/Synapse:syn65471967/wiki/631203.

Original language	English
Title of host publication	35th IEEE International Workshop on Machine Learning for Signal Processing
Subtitle of host publication	Signal Processing in the Age of Lorge Language Models, MLSP 2025
Publisher	IEEE Computer Society
ISBN (Electronic)	9798331570293
DOIs	https://doi.org/10.1109/MLSP62443.2025.11204261
Publication status	Published - 2025
Event	35th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2025 - Istanbul, Turkey Duration: 31 Aug 2025 → 3 Sept 2025

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	35th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2025
Country/Territory	Turkey
City	Istanbul
Period	31/08/25 → 3/09/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

classification
computed tomography
deep learning
Liver vascular tree
segmentation

Access to Document

10.1109/MLSP62443.2025.11204261

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Toprak, T., Yazici, Z. A., Öksüz, I., Koska, I. Ö., Bulucu, P., Gezer, N. S., Beşenk, U., Kavur, A. E., Conze, P. H., Ekenel, H. K., Dicle, O., & Selver, M. A. (2025). Veela Challenge - Vessel Extraction and Extrication for Liver Analysis. In 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). IEEE Computer Society. https://doi.org/10.1109/MLSP62443.2025.11204261

Toprak, Tuǧçe ; Yazici, Ziya Ata ; Öksüz, Ilkay et al. / Veela Challenge - Vessel Extraction and Extrication for Liver Analysis. 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025. IEEE Computer Society, 2025. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

@inproceedings{f9150d7f105b4d3eb849f062666530f6,

title = "Veela Challenge - Vessel Extraction and Extrication for Liver Analysis",

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.1https://www.synapse.org/Synapse:syn65471967/wiki/631203.",

keywords = "classification, computed tomography, deep learning, Liver vascular tree, segmentation",

author = "Tuǧ{\c c}e Toprak and Yazici, \{Ziya Ata\} and Ilkay {\"O}ks{\"u}z and Koska, \{Ilker {\"O}zg{\"u}r\} and Pervin Bulucu and Gezer, \{N. Sinem\} and Ufuk Be{\c s}enk and Kavur, \{A. Emre\} and Conze, \{Pierre Henri\} and Ekenel, \{Hazim Kemal\} and Oǧuz Dicle and Selver, \{M. Alper\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 35th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2025 ; Conference date: 31-08-2025 Through 03-09-2025",

year = "2025",

doi = "10.1109/MLSP62443.2025.11204261",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

publisher = "IEEE Computer Society",

booktitle = "35th IEEE International Workshop on Machine Learning for Signal Processing",

address = "United States",

}

Toprak, T, Yazici, ZA, Öksüz, I, Koska, IÖ, Bulucu, P, Gezer, NS, Beşenk, U, Kavur, AE, Conze, PH, Ekenel, HK, Dicle, O & Selver, MA 2025, Veela Challenge - Vessel Extraction and Extrication for Liver Analysis. in 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025. IEEE International Workshop on Machine Learning for Signal Processing, MLSP, IEEE Computer Society, 35th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2025, Istanbul, Turkey, 31/08/25. https://doi.org/10.1109/MLSP62443.2025.11204261

Veela Challenge - Vessel Extraction and Extrication for Liver Analysis. / Toprak, Tuǧçe; Yazici, Ziya Ata; Öksüz, Ilkay et al.
35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025. IEEE Computer Society, 2025. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Veela Challenge - Vessel Extraction and Extrication for Liver Analysis

AU - Toprak, Tuǧçe

AU - Yazici, Ziya Ata

AU - Öksüz, Ilkay

AU - Koska, Ilker Özgür

AU - Bulucu, Pervin

AU - Gezer, N. Sinem

AU - Beşenk, Ufuk

AU - Kavur, A. Emre

AU - Conze, Pierre Henri

AU - Ekenel, Hazim Kemal

AU - Dicle, Oǧuz

AU - Selver, M. Alper

PY - 2025

Y1 - 2025

N2 - 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.1https://www.synapse.org/Synapse:syn65471967/wiki/631203.

AB - 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.1https://www.synapse.org/Synapse:syn65471967/wiki/631203.

KW - classification

KW - computed tomography

KW - deep learning

KW - Liver vascular tree

KW - segmentation

UR - https://www.scopus.com/pages/publications/105022128370

U2 - 10.1109/MLSP62443.2025.11204261

DO - 10.1109/MLSP62443.2025.11204261

M3 - Conference contribution

AN - SCOPUS:105022128370

T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - 35th IEEE International Workshop on Machine Learning for Signal Processing

PB - IEEE Computer Society

T2 - 35th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2025

Y2 - 31 August 2025 through 3 September 2025

ER -

Toprak T, Yazici ZA, Öksüz I, Koska IÖ, Bulucu P, Gezer NS et al. Veela Challenge - Vessel Extraction and Extrication for Liver Analysis. In 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025. IEEE Computer Society. 2025. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP62443.2025.11204261

Veela Challenge - Vessel Extraction and Extrication for Liver Analysis

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Keywords

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