Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends

Deep learning-based segmentation has become a key tool for the precise and automated analysis of anatomical structures, such as bones, cartilage, and muscles, in musculoskeletal (MSK) imaging. This study examined the research trends by analyzing the number of related publications in PubMed since 2016 from both clinical and technical perspectives. Early studies primarily focused on the segmentation of major anatomical structures such as the spine and knee using large-scale datasets. However, recent studies have expanded to include the extremities and shoulders. In lesion segmentation, traditional topics such as body composition analysis, fractures, and tumors remain prominent, whereas deep learning-based detection and classification methods are increasingly integrated, leading to applications in newer areas. In addition, this study explored commonly used segmentation techniques and various applications of deep learning in MSK imaging. By systematically analyzing trends in deep learning-based segmentation research, we aim to provide insights into future directions for this rapidly evolving field.