Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.

I. Introduction

Cerebrovascualr angiographies can provide insight into abnormality or angioma of cerebral vessels. Segmentation of cerebral vessels in these angiograms is essential for clinical applications, such as cerebrovascular disease diagnosis and surgical planning. Also, it is significant to analytical tasks like vascular structure reconstruction and measurement. TOF-MRA (time-of-flight magnetic resonance angiography) is a preferred non-invasive and in-vivo angiography. This work studies the cerebrovascular segmentation in 3D TOF-MRA volumes.