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Longitudinal mesoscopic photoacoustic imaging of vascular networks requires accurate image co-registration to assess local changes in growing tumours, but remains challenging due to sparsity of data and scan-to-scan variability. Here, we compared a set of 5 curated co-registration methods applied to 49 pairs of vascular images of mouse ears and breast cancer xenografts. Images were segmented using a generative adversarial network and pairs of images and/or segmentations were fed into the 5 tested algorithms. We show the feasibility of co-registering vascular networks accurately using a range of quality metrics, taking a step towards longitudinal characterization of those complex structures.
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Thierry L. Lefebvre, Paul W. Sweeney, Janek Gröhl, Lina Hacker, Emma L. Brown, Thomas R. Else, Mariam-Eleni Oraiopoulou, Algernon Bloom, David Y. Lewis, Sarah E. Bohndiek, "Enabling longitudinal quantitative photoacoustic mesoscopy of vascular networks through image co-registration," Proc. SPIE PC12842, Photons Plus Ultrasound: Imaging and Sensing 2024, PC128421G (13 March 2024); https://doi.org/10.1117/12.3002744