Automatic vessel segmentation in X-ray angiogram using spatio-temporal fully-convolutional neural network

Abstract

Vessel segmentation from X-ray coronary angiogram (CAG) is essential in computer-aided diagnosis of cardiovascular diseases. Automatic segmentation is a challenging task due to the complex vascular structures and poor quality of CAG images. A new deep learning method is presented to automatically extract coronary arteries from dynamic CAG sequences. A spatio-temporal fully-convolutional neural network (ST-FCN) is designed to provide an effective way for segmenting entire vessel trees from motion sequences. An improved post-processing method subsequently refines the segmentation results by making a good use of spatial connectivity and temporal coherence between the moving CAG images. The ST-FCN model outperformed the state-of-the-art segmentation methods with dice similarity coefficient (DSC) of 0.90, accuracy (AC) of 0.92, and sensitivity (SN) of 0.89. Moreover, the ST-FCN achieved superior results in the stenosis detection task with AC of 0.95, SN of 0.92, specificity of 0.95, F1-score of 0.90 among all the reference approaches. The experimental results demonstrated that the integration of spatial and temporal information into a deep learning framework could enhance the vessel segmentation and might be useful for early detection of cardiovascular diseases.