ReviewFrontiers in bioinformatics2025
Segmentation and modeling of large-scale microvascular networks: a survey.
Review in Frontiers in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
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).
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.