Background and objective: Accurate detection of vessel bifurcation points from mesoscopic whole-brain images plays an important role in reconstructing cerebrovascular networks and understanding the pathogenesis of brain diseases. Existing detection methods are either less accurate or inefficient. In this paper, we propose VBNet, an end-to-end, one-stage neural network to detect vessel bifurcation points in 3D images.
Methods: Firstly, we designed a 3D convolutional neural network (CNN), which input a 3D image and output the coordinates of bifurcation points in this image. The network contains a two-scale architecture to detect large bifurcation points and small bifurcation points, respectively, which takes into account the accuracy and efficiency of detection. Then, to solve the problem of low accuracy caused by the imbalance between the numbers of large bifurcations and small bifurcations, we designed a weighted loss function based on the radius distribution of blood vessels. Finally, we extended the method to detect bifurcation points in large-scale volumes.
Results: The proposed method was tested on two mouse cerebral vascular datasets and a synthetic dataset. In the synthetic dataset, the F1-score of the proposed method reached 96.37%. In two real datasets, the F1-score was 92.35% and 86.18%, respectively. The detection effect of the proposed method reached the state-of-the-art level.
Conclusions: We proposed a novel method for detecting vessel bifurcation points in 3D images. It can be used to precisely locate vessel bifurcations from various cerebrovascular images. This method can be further used to reconstruct and analyze vascular networks, and also for researchers to design detection methods for other targets in 3D biomedical images.
Keywords: 3D convolutional neural networks; 3D volume; Brain vasculature; Mesoscopic brain imaging; Vessel bifurcation point detection.
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