Quantification of vascular networks in photoacoustic mesoscopy
Authors
Lefebvre, Thierry
Sweeney, Paul
Stolz, Bernadette
Groehl, Janek
Hacker, Lina
Huang, Ziqiang
Couturier, Dominique-Laurent
Harrington, Heather
Byrne, Helen
Publication Date
2022-06-01Journal Title
Photoacoustics
ISSN
2213-5979
Publisher
Elsevier
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Brown, E., Lefebvre, T., Sweeney, P., Stolz, B., Groehl, J., Hacker, L., Huang, Z., et al. (2022). Quantification of vascular networks in photoacoustic mesoscopy. Photoacoustics https://doi.org/10.1016/j.pacs.2022.100357
Abstract
Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature. The visual or semi-quantitative 2D measurements typically applied to mesoscopic PAI data fail to capture the 3D vessel network complexity and lack robust ground truths for assessment of accuracy. Here, we developed a pipeline for quantifying 3D vascular networks captured using mesoscopic PAI and tested the preservation of blood volume and network structure with topological data analysis. Ground truth data of in silico synthetic vasculatures and a string phantom indicated that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Segmentation of vessels in breast cancer patient-derived xenografts (PDXs) compared favourably to ex vivo immunohistochemistry. Furthermore, our findings underscore the importance of validating segmentation methods when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.
Sponsorship
Cancer Research UK (C14303/A17197)
Cancer Research UK (C14303/A17197)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
Cancer Research UK (C47594/A29448)
Wellcome Trust (204845/Z/16/Z)
Embargo Lift Date
2025-04-21
Identifiers
External DOI: https://doi.org/10.1016/j.pacs.2022.100357
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336343
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