Research On Coronary Artery Segmentation Based On Semi-supervised Deep Learning

Coronary artery segmentation plays an important role in the analysis of vascular anatomical structure,clinical diagnosis of coronary artery diseases(such as coronary artery stenosis and calcification),surgery planning,and blood flow simulation.Currently,coronary artery segmentation methods mainly include active contour model,region growing method,matched filtering method and machine learning method.However,due to the similar intensity of many organs(such as heart chambers,pulmonary arteries and veins)and coronary arteries in CTA images,image noise caused by uneven distribution of contrast agent,and motion artifacts caused by heart beating,it is still difficult to accurately segment coronary artery.Aiming at this challenge,we propose a coronary artery segmentation method from computed tomography angiography(CTA)images based on semi-supervised deep learning.Firstly,we construct a convolutional neural network to recognize the 2D slices that contain coronary artery pixels,then an improved multiscale spatial decomposition network was developed to segment the coronary artery in these 2D slices,finally we obtain 3D coronary artery structures from combining those results in 2D slices.The research work of this paper can be summarized as follows:(1)To alleviate the influence of tissues and organs that are similar to coronary artery structures on the segmentation results,we use the convolutional neural network with attention mechanism to recognize slices that contain coronary artery pixels,which replaces the previous heart region segmentation and provides a region of interest for the pixel-level coronary artery segmentation.The convolutional neural network used in this paper is based on the VGG structure,and the convolutional block attention module(CBAM)is adopted after its convolutional layer.The regularization term is added to the loss function to avoid over-fitting of the network model.In the coronary artery slices recognition experiment,the accuracy,sensitivity and specificity of the classification results were 87%,86% and 87% respectively,which were better than the contrasted methods including Support Vector Machine,Random Forest,Alex Net,Inception V3 and VGG.(2)To solve the problem that only a small number of cardiac CTA images have voxel-level coronary artery labels,we propose a semi-supervised coronary artery segmentation method based on multiscale spatial decomposition network.The network consists of two parts: 1)the decomposer can decompose an input image into a spatial representation containing anatomical information and a latent representation of imaging features,2)the reconstructor learns to reconstruct the input image using the decomposed representations.The network model is trained by the total loss function that made up with the supervised loss function and unsupervised loss function.In order to obtain more abundant information of coronary artery structure,we add multiscale dilated convolution modules,skipping connections and dense connection modules to the network structure.We have proved the effectiveness of the improved modules through the segmentation results of coronary artery in the 2D slices.We combine the results of 2D slices segmentation and finally achieved 3D result of coronary artery segmentation in CTA images,and the sensitivity,specificity,accuracy,positive predictive value,negative predictive value and Dice coefficient were 86.86%,99.79%,99.99%,66.12%,99.99%,and 0.7216,which were better than the contrasted methods including Vesselness,Bi?Guass,FCNRes,3D U-Net and SDNet.The experiments have proved the effectiveness of the proposed method that combines convolutional neural network and multiscale spatial decomposition network in the segmentation of coronary artery.