Deep Learning for Coronary Artery Segmentation in X-ray Angiograms Using a Patch-based Training

摘要

This paper presents a new method for coronary artery segmentation in X-ray angiograms based on deep learning and a patch-based training. The blood vessel segmentation is performed using the U-Net convolutional neural network, which has been trained using patches extracted from the original angiograms instead of using complete images. The publicly available database of coronary angiograms DCA1 containing 130 angiograms with their respective ground-truth has been used to generate the training patterns and subsequently to evaluate and compare the segmentation performance of the proposed method. The hyper-parameter configuration used for training the U-Net parameters has been selected from 90 possible combinations according to five binary classification metrics. Each combination involving the selection of a patch size, weight assigned to the blood vessel class, and learning rate used by the optimization method, has been used in order to train the U-Net parameters with patterns extracted from a set of 100 images. The segmentation performance of the proposed method is compared with five specialized blood vessel segmentation methods from the state of the art using a test set of 30 images, achieving the highest accuracy (0.977) and Dice similarity coefficient (0.779). Moreover, the experimental results have also shown that the proposed method is suitable to be integrated into a computer-aided system to support decision making in medical practice.