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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Automatic MRI-based rotator cuff muscle segmentation using U-Nets.

Ehsan Alipour^1,2, Majid Chalian³, Atefe Pooyan¹

¹Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA.

Skeletal Radiology

|September 12, 2023

Summary

This summary is machine-generated.

A new AI model accurately segments rotator cuff muscles in shoulder MRIs, aiding in injury assessment and treatment planning. The model shows high precision, comparable to radiologists, especially for muscles with clear boundaries.

Keywords:

Artificial intelligence Residual U-Net Segmentation Shoulder rotator cuff

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Musculoskeletal anatomy

Background:

The rotator cuff (RC) is vital for shoulder function and stability.
RC injuries affect 8% of US adults, causing significant disability.
Accurate muscle segmentation aids in evaluating muscle quality and treatment planning.

Purpose of the Study:

To develop and evaluate a deep learning model for segmenting rotator cuff muscles.
To assess the model's accuracy in delineating individual RC muscles from MRI scans.

Main Methods:

A residual deep convolutional encoder-decoder U-net model was developed.
The model was trained and tested on shoulder MRIs from 157 individuals (79 healthy, 78 with RC tears).
Model performance was quantified using the Dice coefficient.

Main Results:

The best model achieved high Dice coefficients: 89% for supraspinatus, 86% for subscapularis, 86% for infraspinatus, and 78% for teres minor.
The model demonstrated satisfactory accuracy in segmenting all rotator cuff muscles.
Performance was superior for muscles with well-defined boundaries.

Conclusions:

The developed AI models can segment rotator cuff muscles with radiologist-level precision.
The algorithm is effective for clinical applications, particularly in evaluating muscle quality and guiding treatment.
The study highlights the potential of AI in improving shoulder injury diagnosis and management.