Muscle Segmentation for Orthopedic Interventions.

Area of Science:

• Medical Imaging
• Computational Anatomy
• Deep Learning

Background:

• Skeletal muscle segmentation is vital for orthopedic interventions.
• Accurate segmentation aids in surgical planning and analysis.
• 3D CT imaging provides detailed anatomical data.

Purpose of the Study:

• To describe two distinct skeletal muscle segmentation methods for 3D CT images.
• To compare computational anatomy-based and deep learning-based approaches.
• To highlight the application of these methods in orthopedic interventions.

Main Methods:

• Computational anatomy-based segmentation models muscle shape and its anatomical course.
• Deep learning-based segmentation directly identifies muscle regions automatically.
• Both methods analyze muscle shape, area, volume, and texture features.

Main Results:

• Both computational anatomy and deep learning methods successfully segment skeletal muscles from 3D CT images.
• Segmentation yields detailed information on muscle shape, volume, and texture.
• Site-specific segmentation examples include cervical, thoracoabdominal, and surface/deep muscles.

Conclusions:

• The choice of segmentation method should align with specific orthopedic intervention requirements.
• These segmentation techniques offer valuable tools for orthopedic applications.
• Further application in orthopedic interventions is feasible and promising.