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

Efficient algorithms are needed to segment vasculature in new 3D medical imaging datasets at scale for research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive whereas computational approaches have limited accuracy. We organize a global machine learning competition, engaging 1,401 participants, to promote development of deep learning methods for 3D blood vessel segmentation in Hierarchical Phase-Contrast Tomography (HiP-CT) datasets. This paper presents a meta-analysis of the top-performing solutions, focusing on segmentation accuracy and morphological analysis. The competition and subsequent analysis reveal convergent methodological innovations: pseudo-labeling approaches that exploit data distributions, metrics and loss functions that optimize for vessel surface and topology, and multi-scale approaches that handle data heterogeneity. Additionally, the paper presents techniques for building deep learning models for the defined task, metrics to assess and compare algorithm performance, and a dataset with manually annotated and curated gold standard segmentations for future studies in blood vessel segmentation within HiP-CT imaging.