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Updated: Jun 16, 2026
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3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
Published on: November 27, 2017
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Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.
Journal of the Korean Society of Radiology
|October 20, 2025
Summary
Deep learning is revolutionizing musculoskeletal (MSK) imaging analysis. Research trends show a shift from major structures to extremities, integrating detection and classification for advanced applications.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Deep learning-based segmentation is crucial for automated analysis of anatomical structures in musculoskeletal (MSK) imaging.
- Previous research focused on segmenting major structures like the spine and knee.
Purpose of the Study:
- To analyze research trends in deep learning-based segmentation for MSK imaging since 2016.
- To provide insights into future research directions in this field.
Main Methods:
- Systematic analysis of PubMed publications related to deep learning in MSK imaging.
- Examination of research trends from clinical and technical perspectives.
- Exploration of segmentation techniques and applications.
Main Results:
- Early studies focused on large-scale segmentation of spine and knee; recent studies expanded to extremities and shoulders.
- Lesion segmentation remains prominent, with increasing integration of deep learning detection and classification.
- Newer applications are emerging due to advancements in deep learning methods.
Conclusions:
- Deep learning-based segmentation is a rapidly evolving field with expanding applications in MSK imaging.
- Future research will likely focus on more complex anatomical regions and advanced detection/classification tasks.
- The integration of deep learning is transforming the analysis of MSK conditions.
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