The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning
Abstract This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the Alex...
Saved in:
Main Authors: | Lina Dai, Md Gapar Md Johar, Mohammed Hazim Alkawaz |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-11-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-80441-y |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Retrospective MRI analysis of 418 adult shoulder joints to assess the physiological morphology of the glenoid in a low-grade osteoarthritic population
by: Marc-Pascal Meier, et al.
Published: (2025-01-01) -
How is infection diagnostic criteria for shoulder periprosthetic joint infection reported in literature: systematic review
by: Alexis L. Clifford, BS, et al.
Published: (2025-01-01) -
Does preoperative forward elevation weakness affect clinical outcomes in anatomic or reverse total shoulder arthroplasty patients with glenohumeral osteoarthritis and intact rotator cuff?
by: Keegan M. Hones, et al.
Published: (2024-07-01) -
The clinical impact of glenoid concavity and version on anterior shoulder stability
by: Sebastian Oenning, MD, et al.
Published: (2025-01-01) -
Design and Simulation of Shoulder Joint Rehabilitation Robot based on the Method of Nerve Repairment
by: Zhao Yuanhao, et al.
Published: (2016-01-01)