Automated Coronary Tree Segmentation for X-ray Angiography Sequences Using Fully-convolutional Neural Networks

Cardiovascular disease is the number one killer that threatens human health. Analysis of coronary vascular geometry plays an important role in the diagnosis of cardiovascular disease. In order to make reliable judgments for clinical usage, accurate and robust vascular segmentation methods are needed. We present an improved segmentation approach for dynamic coronary angiography sequences, which can obtain blood vessel trees in real time. We start with a pre-trained convolutional neural network (CNN) on ImageNet as the base network for initialization. We then adapt this network to the structure of one-shot video object segmentation (OSVOS) to obtain the parent network, which is trained on 50 X-ray angiography sequences for extracting blood vessels. Finally, the model is further fine-tuned using 10 specific coronary angiography sequences to achieve real-time vascular segmentation. Our method achieved the Dice index of 0.88, suggesting that the presented method can be potentially useful in developing a real-time segmentation method for coronary angiography sequences.