Ultrasound imaging modality is used prominently for breast cancer screening and diagnosis because of its safety, portability, ease of use and low cost. Over the years, computer-assisted algorithms have been used to aid the radiologists for interpreting the ultrasound images. The presence of speckle adversely affects the ultrasound image quality because of which accurate segmentation of tumors has become a challenging task. In the present work, various machine learning (ML) and deep learning (DL) based approaches designed for segmenting breast ultrasound images have been reviewed over the past two decades using a characterization approach in terms of (a) datasets used, (b) pre-processing methods, (c) augmentation methods, (d) segmentation methods and (e) evaluation metrics used for the segmentation algorithms along with their brainstorming diagrams. The review presents the achievements made till date in the design of ML and DL based segmentation methods applied to breast ultrasound images and also highlights the directions in which the future research could be carried out.

超声成像方式因其安全性，便携性，易用性和低成本而被广泛用于乳腺癌的筛查和诊断。多年来，已使用计算机辅助算法来帮助放射科医生解释超声图像。斑点的存在对超声图像质量产生不利影响，因为肿瘤的精确分割已成为一项艰巨的任务。在当前工作中，过去二十年来，使用特征化方法对（a）所使用的数据集，（b）预定义的各种基于机器学习（ML）和深度学习（DL）的用于分割乳房超声图像的方法进行了回顾。 -处理方法，（c）扩充方法，（d）分割方法和（e）用于分割算法的评估指标及其头脑风暴图。