多阶段冠状动脉CTA三维分割算法

Multi-stage Three-dimensional Segmentation Algorithm for Coronary CTA

冠脉血管CTA(computed tomography angiongraphy)的分割是判断血管堵塞的首要一步,也是后续三维重建、定性分析等医学诊断的先决条件。本文提出多阶段方式完成冠状动脉从粗到细逐级分割。为了减少非心脏组织给神经网络训练带来的影响,首先采用基于自适应阈值的方法预提取心脏区域。然后提出以V-net作为基础网络框架的深度全卷积网络,扩大了每一层卷积核的第三维通道,充分利用血管空间连续性,增加了网络学习能力。第一阶段提取的心脏区域结合对应标签作为下阶段全卷积网络的训练数据,来实现精确的冠脉血管分割,最后通过水平集函数迭代优化血管边缘轮廓,得到分割结果。本文提出的方法对血管分割的平均Jaccard达到了0.813,Dice达到了0.903,能够对冠脉CTA进行准确的三维分割。

The segmentation of coronary tomography(CTA) is the first step in judging vascular occlusion, and it is also a prerequisite for subsequent medical diagnosis such as three-dimensional reconstruction and qualitative analysis. This paper proposes a multi-stage method to complete the segmentation of coronary arteries from coarse to fine. In order to reduce the impact of non-cardiac tissue on neural network training, the adaptive threshold-based method is first used to pre-extract the heart region. Then, a deep full-convolution network with V-net as the basic network framework is proposed, which expands the third-dimensional channe of the convolution kernel at each layer, makes full use of the continuity of blood vessels, and increases the network learning ability. The heart region extracted in the first stage is combined with the corresponding label as the training data of the next-stage full convolution network to achieve accurate coronary vessel segmentation. Finally, the blood vessel edge contour is iteratively optimized by the level set function to obtain the segmentation result.The proposed method has an average Jaccard of 0.813 for vessel segmentation and a Dice of 0.903, which enables accurate three-dimensional segmentation of coronary CTA.