Scaling up fine-grained intracranial vessel annotations in computed tomography angiography

Chu-Hsuan Lin, Alberto Mario Ceballos Arroyo, Jisoo Kim, Shrikanth Yadav, Huaizu Jiang, Lei Qin, Geoffrey Young
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:1117-1139, 2026.

Abstract

In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by dynamic 4D-CTA scans, we generate segmentation traces for arteries and veins. We then use intensity-guided region growing to obtain segmentations of the majority of vascular territories in the human brain, which are refined and annotated with 20 unique arterial classes by an expert radiologist. Unlike existing datasets, where minor arteries are discarded as background content, we merge these minor arteries into a generic arterial class. Due to the multiple-phase acquisition of dynamic 4D-CTA, labels for a single phase can be re-used for other phases in the same series, greatly increasing the size of our dataset with no additional annotation cost. The results show that models trained with the additional generic artery class produce better fine-grained segmentations across the board. Code and weights, as well as instructions to access the data, are available on: https://github.com/alceballosa/robust-vessel-segmentation

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-lin26a, title = {Scaling up fine-grained intracranial vessel annotations in computed tomography angiography}, author = {Lin, Chu-Hsuan and Arroyo, Alberto Mario Ceballos and Kim, Jisoo and Yadav, Shrikanth and Jiang, Huaizu and Qin, Lei and Young, Geoffrey}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {1117--1139}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/lin26a/lin26a.pdf}, url = {https://proceedings.mlr.press/v340/lin26a.html}, abstract = {In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by dynamic 4D-CTA scans, we generate segmentation traces for arteries and veins. We then use intensity-guided region growing to obtain segmentations of the majority of vascular territories in the human brain, which are refined and annotated with 20 unique arterial classes by an expert radiologist. Unlike existing datasets, where minor arteries are discarded as background content, we merge these minor arteries into a generic arterial class. Due to the multiple-phase acquisition of dynamic 4D-CTA, labels for a single phase can be re-used for other phases in the same series, greatly increasing the size of our dataset with no additional annotation cost. The results show that models trained with the additional generic artery class produce better fine-grained segmentations across the board. Code and weights, as well as instructions to access the data, are available on: https://github.com/alceballosa/robust-vessel-segmentation} }
Endnote
%0 Conference Paper %T Scaling up fine-grained intracranial vessel annotations in computed tomography angiography %A Chu-Hsuan Lin %A Alberto Mario Ceballos Arroyo %A Jisoo Kim %A Shrikanth Yadav %A Huaizu Jiang %A Lei Qin %A Geoffrey Young %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-lin26a %I PMLR %P 1117--1139 %U https://proceedings.mlr.press/v340/lin26a.html %V 340 %X In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by dynamic 4D-CTA scans, we generate segmentation traces for arteries and veins. We then use intensity-guided region growing to obtain segmentations of the majority of vascular territories in the human brain, which are refined and annotated with 20 unique arterial classes by an expert radiologist. Unlike existing datasets, where minor arteries are discarded as background content, we merge these minor arteries into a generic arterial class. Due to the multiple-phase acquisition of dynamic 4D-CTA, labels for a single phase can be re-used for other phases in the same series, greatly increasing the size of our dataset with no additional annotation cost. The results show that models trained with the additional generic artery class produce better fine-grained segmentations across the board. Code and weights, as well as instructions to access the data, are available on: https://github.com/alceballosa/robust-vessel-segmentation
APA
Lin, C., Arroyo, A.M.C., Kim, J., Yadav, S., Jiang, H., Qin, L. & Young, G.. (2026). Scaling up fine-grained intracranial vessel annotations in computed tomography angiography. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:1117-1139 Available from https://proceedings.mlr.press/v340/lin26a.html.

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