Segmentation Of Atherosclerotic Plaque Components From Intravascular Optical Cohernence Tomopraghy Imaging

Coronary heart disease is the world’s leading cause of death,and its diagnosis has always been one of the most concerned issues in cardiovascular field.The rupture of unstable atherosclerotic plaque is the direct cause of coronary heart disease,and the plaque components and morphology determine its stability or vulnerability.Intravascular optical coherence tomography(OCT)has become an important method for characterizing the components of coronary atherosclerotic plaque due to its high resolution and effective identification of different tissues.However,the current characterization of plaque components still depends on the interpretation of large datasets by well-trained observers.It requires huge costs of time and labor,and more importaly,it is difficult to obtain a unified segmentation result due to lowinterobserver variability.This project aims to develop a method based on convolutional neural network(CNN)to automatically extract tissue features from OCT images to characterize the three main components of coronary atherosclerotic plaques(fibrous,lipid and calcification tissues).The OCT data used in this project comes from 31 patients in Nanjing Drum Tower Hospital,including 50 pullbacks of coronary artery data in vivo.40 pullbacks from 27 patients were used for the training of the CNN,and 10 pullbacks from 4 patients were used to test the performance of the CNN after the training was completed.The final training set contains 2000 OCT images with atherosclerotic plaques in total,which are made into 120,000 patches.After necessary preprocessing of the coronary OCT images,the algorithm used a noval CNN architecture called Two-pathway CNN,which is utilized in a cascaded structure.According to the evalution results,the method is effective and reliable in characterizing coronary artery plaque components in in vivo OCT imaging,with the performance of 0.86 in F1-score and 0.88 in accuracy in average.From the results of Wilcoxon’s signed rank test,the performance of the Two-pathway CNN architecture and cascade structure showed significant improvement in performance(p < 0.05).Compared with traditional CNN methods and machine learning methods,CNN with cascade structure can greatly improve the performance of characterization.In conclusion,we believe that the method based on Two-pathway CNN and cascade structure has a higher efficiency in characterizing atherosclerotic plaque,and is expected to be a promising practical method for future clinical practice.