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.

中文翻译：

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3D TOF-MRA 体积中脑血管分割的注意力辅助对抗模型


飞行时间磁共振血管造影 (TOF-MRA) 体积中的脑血管分割对于各种诊断和分析应用至关重要。然而，3D TOF-MRA 中的精确脑血管分割面临着多种问题，包括脑血管形态和强度的巨大变化、背景噪声以及前景脑血管与背景之间的严重类别不平衡。在这项工作中，提出了一种称为 A-SegAN 的 3D 对抗网络模型来分割 TOF-MRA 体积中的脑血管。所提出的模型由用于预测分割图的分割网络 A-SegS 和用于区分预测与真实情况的批评网络 A-SegC 组成。基于该模型，上述问题可以通过流行的视觉注意机制来解决。首先，尽管脑血管具有不同的外观，但 A-SegS 与特征注意块相结合，以过滤掉有区别的特征图。其次，利用硬样本注意力损失来增强 A-SegS 在硬样本上的训练。此外，A-SegC 与输入注意层相结合，以重视前景脑血管类别。所提出的方法在自行构建的体素注释脑血管TOF-MRA分割数据集上进行了评估，实验结果表明，与其他深度学习方法相比，A-SegAN取得了有竞争力或更好的脑血管分割结果，有效缓解了上述问题。