A Left Ventricle Segmentation Based on Boundary Weighted Loss and Residual Feature Aggregation

Abstract

Assessing the left ventricle in cardiac magnetic resonance imaging (MRI) through segmentation plays a crucial role in the diagnosis of cardiac diseases for cardiologists. However, conventional manual segmentation is a tedious task that requires excessive human effort, which makes automated segmentation highly desirable in practice to facilitate the process of clinical diagnosis. This paper proposes a method for automatically outing the left ventricle, namely a left ventricle segmentation algorithm based on boundary weighted loss and residual feature aggregation (RFA). The proposed method is based on the U-Net model, where normal convolutions of the encoder and decoder are replaced with a residual feature aggregation (RFA) module for more efficient feature extraction. At the same time, we add a series of cascaded dilated convolutions in the middle part of the encoder and decoder to expand the receptive field. In addition, we design a boundary weighted loss function, which can effectively address poor segmentation results caused by blurred/incomplete edges of the target object, or high proximity between the target object and others. Through experimental verification, it is proved that the proposed model and the carefully designed loss function both contribute to segmentation performance.