REVIEW article
Front. Bioinform.
Sec. Computational BioImaging
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1645520
Segmentation and Modeling of Large-Scale Microvascular Networks: A Survey
Provisionally accepted
- 1University of Houston, Houston, United States
- 2University of Virginia, Charlottesville, United States
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Recent advances in three-dimensional microscopy enable imaging of whole-organ microvascular networks in small animals. Since microvasculature plays a crucial role in tissue development and function, its structure may provide diagnostic biomarkers and insight into disease progression.However, the microscopy community currently lacks benchmarks for scalable algorithms to measure these potential biomarkers. While many algorithms exist for segmenting vessel-like structures and extracting their surface features and connectivity, they have not been thoroughly evaluated on modern gigavoxel-scale images. In this paper, we propose a comprehensive yet compact survey of available algorithms. We focus on essential features for microvascular analysis, including extracting vessel surfaces and the network's associated connectivity. We select a series of algorithms based on popularity and availability and provide a thorough quantitative analysis of their performance on data sets acquired using light-sheet fluorescence microscopy (LSFM), knife-edge scanning microscopy (KESM), and X-ray microtomography (ยต-CT).
Keywords: segmentation, Skeletonization, vascular, microvascular, network
Received: 11 Jun 2025; Accepted: 22 Aug 2025.
Copyright: ยฉ 2025 Goharbavang, Ashitkov, Pillai, Wythe, Chen and Mayerich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David Mayerich, University of Houston, Houston, United States
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