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

Yashvardhan Jain, Claire L. Walsh, Ekin Yagis, Shahab Aslani, Sonal Nandanwar, Yang Zhou, Juhyung Ha, Katherine S. Gustilo, Joseph Brunet, Shahrokh Rahmani, Paul Tafforeau, Alexandre Bellier, Griffin M. Weber, Peter D. Lee, Katy Börner

Abstract: h3>Abstract</h3> <p>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.</p>

External IDs:doi:10.1101/2024.08.25.609595