一种基于Mask RCNN的融合几何特征的冠状动脉分割方法

A Coronary Artery Segmentation Method Based on Mask RCNN Integrated with Geometric Features

冠状动脉分割是冠心病计算机辅助诊断系统中的一个重要步骤,其目的是保证在后续步骤中只对冠状动脉区域进行处理。冠状动脉CT血管造影(coronary computed tomography angiograph,CCTA)图像具有边界不清、结构复杂、特征不明显等内在特征,这些特点导致CCTA图像分割成为一项困难的任务。针对此问题,提出一种将几何特征融合到Mask RCNN网络中的冠状动脉分割方法,通过边界提取算法和分形特征提取算法提取边界和分形特征。使用冠脉数据集来评估所提出的方法。在评估指标中,所提方法的平均精度(PA)和Dice系数达到83%和(84.0±10.1)%.结果表明,所提方法具有较高的精度和鲁棒性。

Coronary artery segmentation is a major step in coronary heart disease computer-aided diagnosis systems,which is used to ensure only the coronary artery region to be processed in subsequent steps.The coronary computed tomography angiography(CCTA)image has its own inherent characteristics,such as unclear boundary,complicated structure,and inconspicuous features,which make the CCTA image segmentation a difficult task.To address this problem,the fusion of geometric features of the image can enhance the feature learning ability of network,thereby improving the segmentation accuracy.Therefore,we proposed a coronary segmentation method that integrates the geometric features into Mask RCNN.Boundary extraction algorithm and fractal feature extraction algorithm are proposed to extract boundary and fractal features.Through the feature fusion method,the boundary features and fractal features are fused into the network.Experimentally,we evaluated the proposed method by using coronary artery dataset.The average accuracy(PA)and Dice coefficient of the proposed method reached 83%and(84.0±10.1)%,respectively.The test results demonstrate that the proposed method provides greater accuracy and robustness.