# Automatic MRI segmentation for upper limb muscles for clinical applications

> **NIH NIH R21** · NORTHWESTERN UNIVERSITY · 2023 · $177,067

## Abstract

SUMMARY
The clinical value of magnetic resonance (MR) imaging of muscle anatomy and structure is limited by the
bottleneck that arises from muscle segmentation. Thus, clinical evaluations of muscle in rotator cuff injuries to
determine injury and repair potential use a single 2D image at a location selected primarily for consistency of
anatomic landmarks; these methods are fundamentally flawed and do not accurately reflect either total volume
or fatty infiltration for the rotator cuff muscles. Thus, effective, accurate, and fast methods for 3D segmentation
for the upper limb are essential to enable the integration of valuable 3D imaging information into clinical decision-
making. Our long-term goal is to develop a shareable framework that enables accurate, automated segmentation
of MR images of upper limb muscles, on a timescale that makes image analysis tractable for the clinic. The
overall objective is to leverage our fully annotated upper limb MR images in 48 healthy individuals and 10 persons
with rotator cuff tears to develop, assess, and share successful machine learning approaches for both research
and the clinic. Our central hypothesis is that supervised methods trained on our datasets will outperform
unsupervised approaches and the resulting models can be successfully transferred to standard clinical scans.
Our aims are to (1) identify the machine learning techniques with the best accuracy and performance for
automatic segmentation of individual muscles in the upper limb from MR images, and (2) identify model
generalizability and performance for analysis of parasagittal plane images. Our approach is to apply supervised
techniques trained using our unique, existing, manually annotated images that include every muscle that crosses
the shoulder, elbow, and wrist of 48 healthy individuals from three distinct age groups (25-35, 45-60, and 61-83
years) and the shoulder muscles of 10 elderly persons with rotator cuff tears. To strengthen potential translation
to the clinical setting, we must consider application of these methods to the parasagittal plane in which clinical
evaluation of muscle atrophy and fatty infiltration occurs; research scans are typically obtained in the axial plane.
The expected outcomes are shareable models for segmentation of upper limb muscles and a computational
framework to assess performance of a range of algorithms. Further, we expect to determine how effectively
segmentation models, developed from our existing axial tomographic images of shoulder muscles, transfer for
analysis of clinical images acquired to assess atrophy in rotator cuff injury. Accomplishing these objectives will
provide the field the first set of open-source tools for automatic segmentation of upper limb muscles and will
identify the critical next steps for enabling clinical translation.

## Key facts

- **NIH application ID:** 10693854
- **Project number:** 5R21AR080953-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Wendy M Murray
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $177,067
- **Award type:** 5
- **Project period:** 2022-09-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10693854

## Citation

> US National Institutes of Health, RePORTER application 10693854, Automatic MRI segmentation for upper limb muscles for clinical applications (5R21AR080953-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10693854. Licensed CC0.

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