Automated 3D Muscle Segmentation via Deep Learning for MRI Analysis
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Solution Overview
Problem
Manual muscle segmentation from medical images, such as MR and CT scans, is labor-intensive, prone to errors, and requires consensus among clinicians, leading to inaccurate muscle volume and fat fraction calculations, which are crucial for musculoskeletal disorder diagnosis and treatment planning.
Innovation Solution
An automated deep learning-based system for three-dimensional muscle volume segmentation, utilizing machine learning algorithms trained on musculoskeletal images to accurately identify and quantify muscle characteristics like volume and fat fraction, reducing the need for manual tracing and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual muscle segmentation is performed by clinicians, then muscle boundaries can be identified, but the process is labor-intensive and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of clinicians tracing muscle boundaries with an automated deep learning-based image processing system. The system uses neural networks to automatically segment muscles in MRI images, eliminating the need for manual tracing while improving consistency and reducing errors in boundary identification.
2Reliability
If manual muscle segmentation is performed by a group of clinicians, then consensus can be reached on muscle boundaries, but the process becomes even more time-consuming and still error-prone
Solution Approach 1:
The patent implements a self-service automated system where the deep learning model independently performs muscle segmentation without requiring human intervention or consensus. The system processes MRI images and generates muscle boundary identifications autonomously, achieving both high reliability through algorithmic consistency and high productivity by eliminating collaborative review processes.
3Measurement precision
If manual tracing of muscle boundaries is performed, then muscle volume and fat fraction can be calculated, but the results are inaccurate due to unclear muscle boundaries
Solution Approach 1:
The patent replaces the complex manual process of tracing unclear muscle boundaries with an automated deep learning system that handles boundary identification more effectively. The neural network processes the entire image and automatically determines muscle boundaries, leading to more accurate volume and fat fraction calculations while simplifying the overall process.
Data Source
AI summary
A muscle segmentation method and system utilizes a trained machine learning system to identify and segment skeletal muscle from input magnetic resonance images of the shoulder. Properties of the muscle can be determined from the segmentation, and clinical treatment decisions can be more accurately made based on the determined properties.