Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images
The meniscus is a C-shaped connective tissue with a cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for meniscus segmentation from knee magnetic resonance imaging (MRI) images to improve segmen...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2752 |
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| author | Mehmet Ali Şimşek Ahmet Sertbaş Hadi Sasani Yaşar Mahsut Dinçel |
| author_facet | Mehmet Ali Şimşek Ahmet Sertbaş Hadi Sasani Yaşar Mahsut Dinçel |
| author_sort | Mehmet Ali Şimşek |
| collection | DOAJ |
| description | The meniscus is a C-shaped connective tissue with a cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for meniscus segmentation from knee magnetic resonance imaging (MRI) images to improve segmentation performance and evaluate generalization capability. In this study, five different segmentation models were trained, and masks were created from the YOLO series. These masks are combined with pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search. Tests were conducted on internal and external sets and various metrics. The dynamic weighted multiple voting method optimized with grid search performed the best on both the test set (DSC: 0.8976 ± 0.0071, PPV: 0.8561 ± 0.0121, Sensitivity: 0.9467 ± 0.0077) and the external set (DSC: 0.9004 ± 0.0064, PPV: 0.8876 ± 0.0134, Sensitivity: 0.9200 ± 0.0119). The proposed ensemble methods offer high accuracy, reliability, and generalization capability for meniscus segmentation. |
| format | Article |
| id | doaj-art-e3b24ed808fe4569b81c92d49b792f12 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e3b24ed808fe4569b81c92d49b792f122025-08-20T02:05:23ZengMDPI AGApplied Sciences2076-34172025-03-01155275210.3390/app15052752Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI ImagesMehmet Ali Şimşek0Ahmet Sertbaş1Hadi Sasani2Yaşar Mahsut Dinçel3Department of Computer Technologies, Vocational School of Technical Sciences, Tekirdag Namik Kemal University, Tekirdag 59030, TurkeyDepartment of Computer Engineering, Faculty of Engineering, University of Istanbul-Cerrahpasa, Istanbul 34320, TurkeyDepartment of Radiology, Faculty of Medicine, Tekirdag Namik Kemal University, Tekirdag 59030, TurkeyDepartment of Orthopedics and Traumatology, Faculty of Medicine, Tekirdag Namik Kemal University, Tekirdag 59030, TurkeyThe meniscus is a C-shaped connective tissue with a cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for meniscus segmentation from knee magnetic resonance imaging (MRI) images to improve segmentation performance and evaluate generalization capability. In this study, five different segmentation models were trained, and masks were created from the YOLO series. These masks are combined with pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting optimized by grid search. Tests were conducted on internal and external sets and various metrics. The dynamic weighted multiple voting method optimized with grid search performed the best on both the test set (DSC: 0.8976 ± 0.0071, PPV: 0.8561 ± 0.0121, Sensitivity: 0.9467 ± 0.0077) and the external set (DSC: 0.9004 ± 0.0064, PPV: 0.8876 ± 0.0134, Sensitivity: 0.9200 ± 0.0119). The proposed ensemble methods offer high accuracy, reliability, and generalization capability for meniscus segmentation.https://www.mdpi.com/2076-3417/15/5/2752meniscus segmentationmagnetic resonance imagingYOLO seriesensemble methodsvoting methods |
| spellingShingle | Mehmet Ali Şimşek Ahmet Sertbaş Hadi Sasani Yaşar Mahsut Dinçel Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images Applied Sciences meniscus segmentation magnetic resonance imaging YOLO series ensemble methods voting methods |
| title | Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images |
| title_full | Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images |
| title_fullStr | Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images |
| title_full_unstemmed | Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images |
| title_short | Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images |
| title_sort | automatic meniscus segmentation using yolo based deep learning models with ensemble methods in knee mri images |
| topic | meniscus segmentation magnetic resonance imaging YOLO series ensemble methods voting methods |
| url | https://www.mdpi.com/2076-3417/15/5/2752 |
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