Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images
This paper proposes an AI-based diagnostic method using MRI images for rotator cuff injuries to assist in treatment by segmenting tear areas and assessing tear severity. A multi-model deep learning network based on Unet + FPN architecture was developed to automatically segment rotator cuff injury im...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/3/218 |
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| author | Mengqi Li Jingchao Fang Haonan Hou Li Yuan Jin Guo Zhenlong Liu |
| author_facet | Mengqi Li Jingchao Fang Haonan Hou Li Yuan Jin Guo Zhenlong Liu |
| author_sort | Mengqi Li |
| collection | DOAJ |
| description | This paper proposes an AI-based diagnostic method using MRI images for rotator cuff injuries to assist in treatment by segmenting tear areas and assessing tear severity. A multi-model deep learning network based on Unet + FPN architecture was developed to automatically segment rotator cuff injury images and determine tear grades. A dataset of 376 patients with 5640 images was used for training, with an additional 94 patients and 1410 images reserved for testing. To optimize segmentation, a tailored matching strategy was applied, achieving an Intersection over Union (IoU) of 0.79 ± 0.01 and a Dice coefficient of 0.75 ± 0.01, indicating high accuracy in segmenting tear areas. For tear severity indicators, the accuracy of estimating retraction (ER) reached 0.92 ± 0.02, and the accuracy of estimating stop tear width (ESTW) reached 0.79 ± 0.01. As the first AI algorithm specifically developed for diagnosing rotator cuff injuries, this platform demonstrates promising accuracy in both tear segmentation and severity assessment, aiming to support doctors in providing efficient, accurate diagnoses of rotator cuff tears. |
| format | Article |
| id | doaj-art-02e4128b3e8f4e7c9c5c784e688d76a1 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-02e4128b3e8f4e7c9c5c784e688d76a12025-08-20T03:43:21ZengMDPI AGBioengineering2306-53542025-02-0112321810.3390/bioengineering12030218Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI ImagesMengqi Li0Jingchao Fang1Haonan Hou2Li Yuan3Jin Guo4Zhenlong Liu5Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing 100191, ChinaDepartment of Radiology, Peking University Third Hospital, Beijing 100191, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing 100191, ChinaThis paper proposes an AI-based diagnostic method using MRI images for rotator cuff injuries to assist in treatment by segmenting tear areas and assessing tear severity. A multi-model deep learning network based on Unet + FPN architecture was developed to automatically segment rotator cuff injury images and determine tear grades. A dataset of 376 patients with 5640 images was used for training, with an additional 94 patients and 1410 images reserved for testing. To optimize segmentation, a tailored matching strategy was applied, achieving an Intersection over Union (IoU) of 0.79 ± 0.01 and a Dice coefficient of 0.75 ± 0.01, indicating high accuracy in segmenting tear areas. For tear severity indicators, the accuracy of estimating retraction (ER) reached 0.92 ± 0.02, and the accuracy of estimating stop tear width (ESTW) reached 0.79 ± 0.01. As the first AI algorithm specifically developed for diagnosing rotator cuff injuries, this platform demonstrates promising accuracy in both tear segmentation and severity assessment, aiming to support doctors in providing efficient, accurate diagnoses of rotator cuff tears.https://www.mdpi.com/2306-5354/12/3/218MRIrotator cuff injurydeep learningAI segmentationmulti-model mechanism |
| spellingShingle | Mengqi Li Jingchao Fang Haonan Hou Li Yuan Jin Guo Zhenlong Liu Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images Bioengineering MRI rotator cuff injury deep learning AI segmentation multi-model mechanism |
| title | Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images |
| title_full | Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images |
| title_fullStr | Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images |
| title_full_unstemmed | Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images |
| title_short | Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images |
| title_sort | multi model segmentation algorithm for rotator cuff injury based on mri images |
| topic | MRI rotator cuff injury deep learning AI segmentation multi-model mechanism |
| url | https://www.mdpi.com/2306-5354/12/3/218 |
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