On blood vessels and vascular networks

Vascular structures represent a part of the anatomy that attracts particular interest in biomedical imaging. Quantifying the structure of local vessels, as well as the global network, and making efforts to map blood flow and related functional physiological processes, is posing significant computational challenges in biomedical image analysis: Vessels need to be segmented in total to not miss on connections in the graph, foreground and background voxels are highly imbalanced, and training data for supervised learning approaches are tedious to annotate.

I will present our work that improves image segmentation by synthesizing vascular structure for transfer learning, introduces new topological measures for segmenting vascular structures, and extracts network directly from image volumes using a transformer architecture that jointly predicts objects and their relations. I will focus on the extraction of whole brain vessel graphs of mice, capturing the full cerebrovascular network down to the capillary level, and I will comment on related benchmark dataset we made available at Neurips and MICCAI.