Back

VesSynth: Tubes Are All You Need for Robust Cross-Scale Cross-Modal 3D Vessel Segmentation

Mauri, C.; Mckenzie, A.; Analoro, C.; Yeon, E.; Coviello, R.; Mora, J.; Chollet, E.; Deden Binder, L.; Mahar, A.; Lin, S.; Benlahcen, M.; Ream, A.; Jama, A.; Garcia, I.; Tran, N.; Onta, P.; Wood, S.; Willis, A.; Mahmood, A.; Sinoballa, G.; Malki, A.; Tran, K.; Malireddy, V.; Onumajuru, N.; Lakshmanan, S.; Hercules Landaverde, K.; Sidow, R.; Wood, D.; Nguyen, B.; Hernandez, J.; Bernier, M.; Hunter, J.; Malki, A.; Tum, A.; Chavez, V.; Shahu, Z.; Vasi, I.; Visser, A.; Ghaouta, Z.; Bond, F.; Vigneshwaran, R.; Kirkpatrick, E.; Avalos Barbosa, M.; Rauh, K.; Herisse, R.; Garcia Pallares, E.; Zeng, X.

2026-04-06 bioengineering
10.64898/2026.04.01.715909 bioRxiv
Show abstract

The cerebral vasculature is central to brain function, with alterations linked to numerous cerebrovascular and neurological disorders. Yet, no single imaging modality can capture the entire cerebral vascular network in humans. Instead, an array of techniques are sensitized to different spatial scales, while trading off resolution for coverage. Magnetic Resonance Imaging (MRI) typically resolves only large pial vessels, while high-resolution microscopy allows micrometer-scale vessels to be mapped over limited spatial extents. These techniques must therefore be combined to obtain a complete mapping of the cerebral angioarchitecture, which underscores the need for automatic, cross-modal vessel segmentation. Here, we introduce VesSynth, a flexible vessel segmentation framework that achieves state-of-the-art accuracy across multiple modalities and spatial resolutions (MR, optical and X-ray imaging), despite being trained entirely on synthetic data. By enabling consistent vascular mapping across scales, this framework paves the way to comprehensive investigation of cerebrovascular organization and its role in health and disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Methods
336 papers in training set
Top 0.2%
22.5%
2
Nature Communications
4913 papers in training set
Top 18%
10.1%
3
Advanced Science
249 papers in training set
Top 2%
8.4%
4
Nature Medicine
117 papers in training set
Top 0.3%
6.4%
5
IEEE Transactions on Medical Imaging
18 papers in training set
Top 0.1%
6.4%
50% of probability mass above
6
Nature Biomedical Engineering
42 papers in training set
Top 0.2%
4.0%
7
Nature Neuroscience
216 papers in training set
Top 3%
3.1%
8
Science
429 papers in training set
Top 11%
2.4%
9
Science Advances
1098 papers in training set
Top 11%
2.4%
10
Neuron
282 papers in training set
Top 5%
2.1%
11
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 29%
1.9%
12
Science Translational Medicine
111 papers in training set
Top 2%
1.8%
13
Communications Biology
886 papers in training set
Top 9%
1.7%
14
Nature
575 papers in training set
Top 11%
1.7%
15
Cancer Research
116 papers in training set
Top 2%
1.3%
16
Nature Biotechnology
147 papers in training set
Top 5%
1.3%
17
Nature Machine Intelligence
61 papers in training set
Top 2%
1.2%
18
Cell
370 papers in training set
Top 14%
1.2%
19
Scientific Reports
3102 papers in training set
Top 66%
1.2%
20
PLOS ONE
4510 papers in training set
Top 60%
1.2%
21
Light: Science & Applications
16 papers in training set
Top 0.4%
1.2%
22
Cell Reports
1338 papers in training set
Top 29%
0.9%
23
Cell Systems
167 papers in training set
Top 10%
0.9%
24
ACS Photonics
13 papers in training set
Top 0.4%
0.9%
25
Nature Cell Biology
99 papers in training set
Top 5%
0.7%
26
eLife
5422 papers in training set
Top 59%
0.7%
27
Journal of Cell Biology
333 papers in training set
Top 5%
0.6%
28
Medical Image Analysis
33 papers in training set
Top 1%
0.6%
29
Nano Letters
63 papers in training set
Top 3%
0.6%
30
ACS Nano
99 papers in training set
Top 4%
0.6%