Machine learning-based anatomical segmentation: A systematic review of methodologies and applications

Abstract

Image segmentation has established itself as a cornerstone of modern medical imaging practices, achieving state-of-the-art accuracy and playing a pivotal role in critical applications such as pathology diagnosis, treatment planning, tumour segmentation and organ delineation to name a few. The integration of deep learning and neural network techniques has sparked a dynamic and constantly evolving landscape, propelling rapid advancements in image segmentation. This work aims to provide an all encompassing review of the general state-of-the-art, with a focus on developments made within the last few years. Over 100 papers presenting development in the field have been considered, primarily published between 2020-2023, separated into their architectural type. Within each type, significant works and conclusions have been highlighted. We conclude the paper with a discussion of the challenges facing the segmentation landscape as a whole, and how these challenges have been best circumnavigated to date.