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bioRxiv · 10.1101/2024.08.25.609595

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys

Jain, Y.·Walsh, C. L.·Yagis, E.·Aslani, S.·Nandanwar, S.·Zhou, Y.·Ha, J.·Gustilo, K. S.·Brunet, J.·Rahmani, S.·Tafforeau, P.·Bellier, A.·Weber, G. M.·Lee, P. D.·Borner, K.
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Abstract

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focusing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.

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Jain, Y., Walsh, C. L., Yagis, E., Aslani, S., Nandanwar, S., Zhou, Y., Ha, J., Gustilo, K. S., Brunet, J., Rahmani, S., Tafforeau, P., Bellier, A., Weber, G. M., Lee, P. D., Borner, K.. 2024-08-26. Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys. https://doi.org/10.1101/2024.08.25.609595

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