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

arXiv: https://arxiv.org/abs/2606.21756 · HTML: https://mlsanity.com/paper/2606.21756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography](https://mlsanity.com/paper/2312.17670/) (2023)
- [Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures](https://mlsanity.com/paper/2404.03010/) (2024)
- [BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation](https://mlsanity.com/paper/2511.19394/) (2025)
- [An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes](https://mlsanity.com/paper/2204.12333/) (2022)
- [Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification](https://mlsanity.com/paper/2410.15614/) (2024)
- [An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis](https://mlsanity.com/paper/2512.03869/) (2025)
- [Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data](https://mlsanity.com/paper/2602.00391/) (2026)

## Abstract (arXiv)

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. We will make our code, annotation GUI, and model weights available to the scientific community. Code, weights, and data will be made available on https://github.com/alceballosa/robust-vessel-segmentation