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We present DeepVesselNet, an architecture tailored to the challenges facedwhen extracting vessel networks or trees and corresponding features in 3-Dangiographic volumes using deep learning. We discuss the problems of lowexecution speed and high memory requirements associated with full 3-Dconvolutional networks, high-class imbalance arising from the low percentage ofvessel voxels, and unavailability of accurately annotated training data - andoffer solutions as the building blocks of DeepVesselNet.  First, we formulate 2-D orthogonal cross-hair filters which make use of 3-Dcontext information at a reduced computational burden. Second, we introduce aclass balancing cross-entropy loss function with false positive rate correctionto handle the high-class imbalance and high false positive rate problemsassociated with existing loss functions. Finally, we generate synthetic datasetusing a computational angiogenesis model capable of generating vascular treesunder physiological constraints on local network structure and topology and usethese data for transfer learning.  DeepVesselNet is optimized for segmenting and analyzing vessels, and we testthe performance on a range of angiographic volumes including clinical MRA dataof the human brain, as well as X-ray tomographic microscopy scans of the ratbrain. Our experiments show that, by replacing 3-D filters with cross-hairfilters in our network, we achieve over 23% improvement in speed, lower memoryfootprint, lower network complexity which prevents overfitting and comparableaccuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0.07when compared to full 3-D filters). Our class balancing metric is crucial fortraining the network and transfer learning with synthetic data is an efficient,robust, and very generalizable approach leading to a network that excels in avariety of angiography segmentation tasks.

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes