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Automated segmentation of the blood vessels in 3D volumes is an essentialstep for the quantitative diagnosis and treatment of many vascular diseases. 3Dvessel segmentation is being actively investigated in existing works, mostly indeep learning approaches. However, training 3D deep networks requires largeamounts of manual 3D annotations from experts, which are laborious to obtain.This is especially the case for 3D vessel segmentation, as vessels are sparseyet spread out over many slices and disconnected when visualized in 2D slices.In this work, we propose a novel method to segment the 3D peripancreaticarteries solely from one annotated 2D projection per training image with depthsupervision. We perform extensive experiments on the segmentation ofperipancreatic arteries on 3D contrast-enhanced CT images and demonstrate howwell we capture the rich depth information from 2D projections. We demonstratethat by annotating a single, randomly chosen projection for each trainingsample, we obtain comparable performance to annotating multiple 2D projections,thereby reducing the annotation effort. Furthermore, by mapping the 2D labelsto the 3D space using depth information and incorporating this into training,we almost close the performance gap between 3D supervision and 2D supervision.Our code is available at: https://github.com/alinafdima/3Dseg-mip-depth.

3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images