TC-SegNet: robust deep learning network for fully automatic two-chamber segmentation of two-dimensional echocardiography

Abstract

Heart chamber quantification is an essential clinical task to analyze heart abnormalities by evaluating the heart volume estimated through the endocardial border of the chambers. A precise heart chamber segmentation algorithm using echocardiography is essential for improving the diagnosis of cardiac disease. This paper proposes a robust two chamber segmentation network (TC-SegNet) for echocardiography which follows a U-Net architecture and effectively incorporates the proposed modified skip connection, Atrous Spatial Pyramid Pooling (ASPP) modules and squeeze and excitation modules. The TC-SegNet is evaluated on the open-source fully annotated dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS). The proposed TC-SegNet obtained an average value of F1-score of 0.91, an average Dice score of 0.9284 and an IoU score of 0.8322 which are higher than the reference models used here for comparison. Further, Pixel error (PE) of 1.5109 which are significantly less than the comparison models. The segmentation results and metrics show that the proposed model outperforms the state-of-the-art segmentation methods.