# Segmentation and modeling of large-scale microvascular networks: a survey

## TL;DR

This paper reviews and evaluates algorithms for analyzing large-scale microvascular networks imaged with modern 3D microscopy techniques.

## Contribution

The paper provides a comprehensive survey and quantitative benchmark of microvascular segmentation algorithms on gigavoxel-scale datasets.

## Key findings

- Popular algorithms were evaluated on LSFM, KESM, and µ-CT datasets for vessel surface and connectivity extraction.
- The study highlights the lack of benchmarks for scalable microvascular analysis algorithms.
- Performance metrics reveal strengths and limitations of existing methods on large-scale imaging data.

## Abstract

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 datasets acquired using light sheet fluorescence microscopy (LSFM), knife-edge scanning microscopy (KESM), and X-ray microtomography (µ-CT).