Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss

Coronary heart disease is a serious disease that endangers human health and life. In recent years, the incidence and mortality of coronary heart disease have increased rapidly. The quantification of the coronary artery is critical in diagnosing coronary heart disease.

In this paper, we improve the coronary arteries segmentation performance from two aspects of network model and algorithm. We proposed a U-shaped network based on spatio-temporal feature fusion structure to segment coronary arteries from 2D slices of computed tomography angiography (CTA) heart images. The spatio-temporal feature combines features of multiple levels and different receptive fields separately to get more precise boundaries. It is easy to cause over-segmented for the small proportion of coronary arteries in CTA images. For this reason, a combo loss function was designed to deal with the notorious imbalance between inputs and outputs that plague learning models. Input imbalance refers to the class imbalance in the input training samples. The output imbalance refers to the imbalance between the false positive and false negative of the inference model. The two imbalances in training and inference were divided and conquered with our combo loss function. Specifically, a gradient harmonizing mechanism (GHM) loss was employed to balance the gradient contribution of the input samples and at the same time punish false positives/negatives using another sensitivity-precision loss term to learn better model parameters gradually.

Compared with some existing methods, our proposed method improves the segmentation accuracy significantly, achieving the mean Dice coefficient of 0.87. In addition, accurate results can be obtained with little data using our method. Code is available at: https://github.com/Ariel97-star/FFNet-CoronaryArtery-Segmentation .

Our method can intelligently capture coronary artery structure and achieve accurate flow reserve fraction (FFR) analysis. Through a series of steps such as CPR curved reconstruction, the detection of coronary heart disease can be achieved without affecting the patient's body. In addition, our work can be used as an effective means to assist in the detection of stenosis. In the screening of coronary heart disease among high-risk cardiovascular factors, automatic detection of luminal stenosis can be performed based on the application of later algorithm transformation.