Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort
Sarcopenia is characterized by a degeneration of muscle mass and strength that incurs impaired mobility, posing grievous impacts on the quality of life and well-being of older adults worldwide. In 2018, a new international consensus was formulated to incorporate ultrasound imaging of the rectus femo...
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2024-12-01
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| Series: | Bioengineering |
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| author | Dawei Zhang Hongyu Kang Yu Sun Justina Yat Wa Liu Ka-Shing Lee Zhen Song Jien Vei Khaw Jackie Yeung Tao Peng Sai-kit Lam Yongping Zheng |
| author_facet | Dawei Zhang Hongyu Kang Yu Sun Justina Yat Wa Liu Ka-Shing Lee Zhen Song Jien Vei Khaw Jackie Yeung Tao Peng Sai-kit Lam Yongping Zheng |
| author_sort | Dawei Zhang |
| collection | DOAJ |
| description | Sarcopenia is characterized by a degeneration of muscle mass and strength that incurs impaired mobility, posing grievous impacts on the quality of life and well-being of older adults worldwide. In 2018, a new international consensus was formulated to incorporate ultrasound imaging of the rectus femoris (RF) muscle for early sarcopenia assessment. Nonetheless, current clinical RF muscle identification and delineation procedures are manual, subjective, inaccurate, and challenging. Thus, developing an effective AI-empowered RF segmentation model to streamline downstream sarcopenia assessment is highly desirable. Yet, this area of research readily goes unnoticed compared to other disciplines, and relevant research is desperately wanted, especially in comparison among traditional, classic, and cutting-edge segmentation networks. This study evaluated an emerging Automatic Segment Anything Model (AutoSAM) compared to the U-Net and nnU-Net models for RF segmentation on ultrasound images. We prospectively analyzed ultrasound images of 257 older adults (aged > 65) in a community setting from Hong Kong’s District Elderly Community Centers. Three models were developed on a training set (<i>n</i> = 219) and independently evaluated on a testing set (<i>n</i> = 38) in aspects of DICE, Intersection-over-Union, Hausdorff Distance (HD), accuracy, precision, recall, as well as stability. The results indicated that the AutoSAM achieved the best segmentation agreement in all the evaluating metrics, consistently outperforming the U-Net and nnU-Net models. The results offered an effective state-of-the-art RF muscle segmentation tool for sarcopenia assessment in the future. |
| format | Article |
| id | doaj-art-bf2bae40c2df4ad69dd73ebf34a61f13 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-bf2bae40c2df4ad69dd73ebf34a61f132024-12-27T14:11:45ZengMDPI AGBioengineering2306-53542024-12-011112129110.3390/bioengineering11121291Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community CohortDawei Zhang0Hongyu Kang1Yu Sun2Justina Yat Wa Liu3Ka-Shing Lee4Zhen Song5Jien Vei Khaw6Jackie Yeung7Tao Peng8Sai-kit Lam9Yongping Zheng10Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou 215222, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaSarcopenia is characterized by a degeneration of muscle mass and strength that incurs impaired mobility, posing grievous impacts on the quality of life and well-being of older adults worldwide. In 2018, a new international consensus was formulated to incorporate ultrasound imaging of the rectus femoris (RF) muscle for early sarcopenia assessment. Nonetheless, current clinical RF muscle identification and delineation procedures are manual, subjective, inaccurate, and challenging. Thus, developing an effective AI-empowered RF segmentation model to streamline downstream sarcopenia assessment is highly desirable. Yet, this area of research readily goes unnoticed compared to other disciplines, and relevant research is desperately wanted, especially in comparison among traditional, classic, and cutting-edge segmentation networks. This study evaluated an emerging Automatic Segment Anything Model (AutoSAM) compared to the U-Net and nnU-Net models for RF segmentation on ultrasound images. We prospectively analyzed ultrasound images of 257 older adults (aged > 65) in a community setting from Hong Kong’s District Elderly Community Centers. Three models were developed on a training set (<i>n</i> = 219) and independently evaluated on a testing set (<i>n</i> = 38) in aspects of DICE, Intersection-over-Union, Hausdorff Distance (HD), accuracy, precision, recall, as well as stability. The results indicated that the AutoSAM achieved the best segmentation agreement in all the evaluating metrics, consistently outperforming the U-Net and nnU-Net models. The results offered an effective state-of-the-art RF muscle segmentation tool for sarcopenia assessment in the future.https://www.mdpi.com/2306-5354/11/12/1291deep learningmedical segment anything modelrectus femoris muscleSarcopenia UltrasoundU-Net |
| spellingShingle | Dawei Zhang Hongyu Kang Yu Sun Justina Yat Wa Liu Ka-Shing Lee Zhen Song Jien Vei Khaw Jackie Yeung Tao Peng Sai-kit Lam Yongping Zheng Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort Bioengineering deep learning medical segment anything model rectus femoris muscle Sarcopenia Ultrasound U-Net |
| title | Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort |
| title_full | Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort |
| title_fullStr | Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort |
| title_full_unstemmed | Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort |
| title_short | Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net—A Prospective Study of Hong Kong Community Cohort |
| title_sort | rectus femoris muscle segmentation on ultrasound images of older adults using automatic segment anything model nnu net and u net a prospective study of hong kong community cohort |
| topic | deep learning medical segment anything model rectus femoris muscle Sarcopenia Ultrasound U-Net |
| url | https://www.mdpi.com/2306-5354/11/12/1291 |
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