Abstract
In this project, we propose a deep learning-based method to detect calcified tissue within coronary optical coherence tomography (OCT) images. Conventionally, diseased tissue was manually checked on a frame (Bscan)-by-frame (Bscan) basis. Based on faster region-based convolutional neural network, our proposed method can automatically detect the calcified regions from diseased coronary artery. Our method achieves promising result of mean average precision (0.74) and recall (0.79) in detecting calcified regions. The proposed method could provide valuable information for locating calcified tissue within a large volume of OCT images. It has great potential to aid the treatment of coronary artery disease.
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