Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review

Abstract

Ultrasound (US) is generally preferred because it is of low-cost, safe, and non-invasive. US image segmentation is crucial in image analysis. Recently, deep learning-based methods are increasingly being used to segment US images. This survey systematically summarizes and highlights crucial aspects of the deep learning techniques developed in the last five years for US segmentation of various body regions. We investigate and analyze the most popular loss functions and metrics for training and evaluating the neural network for US segmentation. Furthermore, we study the patterns in neural network architectures proposed for the segmentation of various regions of interest. We present neural network modules and priors that address the anatomical challenges associated with different body organs in US images. We have found that variants of U-Net that have dedicated modules to overcome the low-contrast and blurry nature of images are suitable for US image segmentation. Finally, we also discuss the advantages and challenges associated with deep learning methods in the context of US image segmentation.