Echocardiographic image segmentation using deep Res-U network - Ali, Yasser; Janabi-Sharifi, Farrokh; Beheshti, Soosan (Biomedical signal processing and control, 2021)
•A fast and fully automatic deep learning framework for cardiac boundary segmentation with the focus on left ventricle segmentation.•Proposing a network Res-U consisting of a modified version of ResNet-50 to be used as an encoder in the U-net.•Proposing a new strategy of training to improve the accuracy of the proposed model.•The model outperforms other state-of-the-art methods in terms of accuracy with a Dice metric of 0.97 ± 0.01. Cardiac function assessment using echocardiography is a crucial step in daily cardiology. However, cardiac boundary segmentation and in particular, ventricle segmentation is a challenging procedure due to shadows and speckle noise. Manual segmentation of the cardiac boundary is a time-consuming process which rules out conventional segmentation for many situations such as emergency cases and image-guided robotic interventions. Therefore, providing an efficient and robust autonomous segmentation method is crucial for such applications. In this paper, a fast and fully automatic deep learning framework for left ventricle segmentation is proposed. This model couples the advantages of ResNet and U-Net to provide reliable segmentation results. We propose a new encoder in the U-Net, defined as ResU which is a modified version of ResNet-50 and has a superiority over ResNet in data denoising. We trained this model on the dataset CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) which is a large, publicly available and fully annotated dataset for 2D echocardiographic assessment. It is shown that this model outperforms other state-of-the-art methods in terms of accuracy with a Dice metric of 0.97±0.01.