Deep Learning for Medical Ultrasound Image Segmentation: A Systematic Review of the Current Research

Abstract

<p>Deep learning (DL) has enabled automated segmentation of ultrasound images, and due to the rapid development of DL models, we want to offer a comprehensive overview of the current state of research. Following PRISMA 2020 guidelines, we systematically selected and analyzed 296 recent scientific articles on DL-based ultrasound segmentation from the PubMed database. According to our results, the most common targets of DL-based ultrasound segmentation are breast tumors, organs, and cardiovascular structures. Other major application categories include orthopedics, thyroid nodules, obstetrics-gynecology, and oncology in general. Convolutional neural networks (CNNs) and especially U-shaped architectures have preserved their popularity, even though vision transformers (ViTs), CNN/ViT hybrids, and segment anything models have also become well-established within a few years of their release. The newer models are given significantly more data, but no association between the method type and the reported values of the evaluation metrics can be detected across several studies. Most common limitations of the current research include a lack of information on computational requirements and issues related to model performance evaluation. DL-based ultrasound segmentation is a quickly developing field, supported by increased use of ultrasound imaging, new public datasets, and methodological advancements.<br></p>