Back

TopBrain Segmentation Challenge for Whole Brain Vessel Anatomy

Yang, K.; Shi, P.; Huang, H.; Musio, F.; Baazaoui, H.; Aydin, O. U.; Hilbert, A.; Hamadache, R. E.; Yalcin, C.; Zhang, M.; Falcetta, D.; de la Rosa, E.; Shit, S.; Prabhakar, C.; Wittmann, B.; Rokuss, M. R.; Kirchhoff, Y.; Al-Maskari, R.; Hoeher, L.; Juchler, N.; Casamitjana, A.; Cleary, J.; Schmick, A.; Baumgartner, P.; Deseoe, J.; Vandans, O.; Lee, D.; Oh, K.; LaBella, D.; Mazher, M.; Niederer, S. A.; Qayyum, A.; Liu, Y.; Chen, J.; Kim, W.; Asawalertsak, N.; Kim, M.; Shin, D.; Park, S.-H.; Kikuchi, S.; Zhang, Y.; Liu, J.; Cui, Y.; Qiu, Y.; Verschuur, A.; Zhang, J.; van der Schaaf, I.; Su, R.;

2026-05-30 radiology and imaging
10.64898/2026.05.28.26354312 medRxiv
Show abstract

We present the TopBrain 2025 Challenge, the first benchmark for fine-grained multiclass segmentation of the whole brain vasculature in both computed tomography angiography (CTA) and magnetic resonance angiography (MRA). Building on the TopCoW challenge, TopBrain scales vessel annotation from the Circle of Willis to the entire brain, introducing a dataset of 90 annotated volumes across 48 landmark vessel classes spanning arterial and venous systems, of which 50 training volumes are publicly released. Vessel definitions were consolidated from established neuroanatomical references into a unified annotation scheme, and vessel caliber measurements along the centerline are reported for the first time across the whole brain vascular anatomy. To address the unique challenges of multiclass brain vessel segmentation, we propose an evaluation framework that accounts for detection in segmentation performance, assesses anatomical plausibility, and introduces novel contamination metrics that characterize inter-class prediction errors. Fifteen teams from over 220 registered participants submitted algorithms to the benchmark. The top-performing teams built on nnUNet with principled system design choices, achieving around 80% Dice scores, near-zero invalid neighbor counts, over 60% F1 scores for side-road vessels, and below 18% foreground contamination ratio. Larger vessels are easier to segment, while smaller and more complex vessels remain the true bottleneck. The annotated datasets and podium-finish algorithms are made publicly available on Zenodo.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

1
Medical Image Analysis
35 papers in training set
Top 0.1%
14.6%
2
Nature Communications
5641 papers in training set
Top 20%
8.8%
3
Scientific Reports
3612 papers in training set
Top 21%
4.8%
4
NeuroImage
903 papers in training set
Top 3%
4.3%
5
Nature Machine Intelligence
70 papers in training set
Top 0.9%
3.2%
6
Imaging Neuroscience
282 papers in training set
Top 2%
3.2%
7
GigaScience
212 papers in training set
Top 1%
2.7%
8
IEEE Transactions on Medical Imaging
21 papers in training set
Top 0.2%
2.7%
9
Scientific Data
209 papers in training set
Top 1%
2.4%
10
European Radiology
15 papers in training set
Top 0.3%
2.4%
11
Nature Computational Science
55 papers in training set
Top 0.3%
2.4%
50% of probability mass above
12
Frontiers in Computational Neuroscience
60 papers in training set
Top 0.6%
2.4%
13
Communications Biology
993 papers in training set
Top 9%
2.4%
14
Fluids and Barriers of the CNS
28 papers in training set
Top 0.2%
2.4%
15
PLOS ONE
5266 papers in training set
Top 43%
2.4%
16
Magnetic Resonance in Medicine
85 papers in training set
Top 0.4%
2.4%
17
Medical Physics
14 papers in training set
Top 0.3%
1.9%
18
Nature Medicine
125 papers in training set
Top 1%
1.9%
19
Journal of Medical Imaging
11 papers in training set
Top 0.1%
1.9%
20
Human Brain Mapping
329 papers in training set
Top 3%
1.7%
21
npj Digital Medicine
118 papers in training set
Top 2%
1.7%
22
Nature
645 papers in training set
Top 7%
1.5%
23
Brain Informatics
10 papers in training set
Top 0.1%
1.5%
24
Neuro-Oncology Advances
25 papers in training set
Top 0.4%
1.1%
25
IEEE Access
35 papers in training set
Top 1%
1.1%
26
Journal of Cerebral Blood Flow & Metabolism
42 papers in training set
Top 0.5%
1.1%
27
Frontiers in Neuroscience
256 papers in training set
Top 5%
1.1%
28
Aperture Neuro
20 papers in training set
Top 0.4%
1.0%
29
NeuroImage: Clinical
144 papers in training set
Top 2%
1.0%
30
Frontiers in Artificial Intelligence
20 papers in training set
Top 0.6%
1.0%