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2024, Vol. 28 ›› Issue (33): 5382-5387

Role and significance of deep learning in intelligent segmentation and measurement analysis of knee osteoarthritis MRI images

Yu Guangwen1, 2, Xie Junjie1, Liang Jiajian1, Liu Wengang2, Wu Huai2, Li Hui2, Hong Kunhao2, Li Anan2, Guo Haopeng2   

  1. 1Fifth Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510095, Guangdong Province, China; 2Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou 510095, Guangdong Province, China

  • Received:2023-08-28 Accepted:2023-11-13 Online:2024-11-28 Published:2024-01-31

  • Contact: Liu Wengang, MD, Chief physician, Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou 510095, Guangdong Province, China

  • About author:Yu Guangwen, Master, Attending physician, Fifth Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510095, Guangdong Province, China; Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou 510095, Guangdong Province, China

  • Supported by:

    Research Project of Traditional Chinese Medicine Bureau of Guangdong Province, No. 20241025 (to YGW); Basic and Applied Basic Research Foundation of Guangdong Province, No. 2023A1515012615, No. 2022A1515220157 (to LWG)


Abstract: BACKGROUND: MRI is important for the diagnosis of early knee osteoarthritis. MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence.
OBJECTIVE: Through deep learning of MRI images of knee osteoarthritis, the segmentation of femur, tibia, patella, cartilage, meniscus, ligaments, muscles and effusion of knee can be automatically divided, and then volume of knee fluid and muscle content were measured.
METHODS: 100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset (n=160), validation dataset (n=20), and test dataset (n=20) according to the ratio of 8:1:1. The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model. A Coarse MRI segmentation model of the knee sagittal plane was trained first, and the rough segmentation results were used as a mask, and then the fine segmentation model was trained. The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input, and DeepLab v3 was used to segment bone, cartilage, ligament, meniscus, muscle, and effusion of knee, and 3D reconstruction was finally displayed and automatic measurement results (muscle content and volume of knee fluid) were displayed to complete the deep learning application program. The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation.
RESULTS AND CONCLUSION: (1) The 26 normal subjects were selected, including 13 females and 13 males, with a mean age of (34.88±11.75) years old. The mean muscle content of the knee joint was (1 051 322.94±2 007 249.00) mL, the mean median was 631 165.21 mL, and the mean volume of effusion was (291.85±559.59) mL. The mean median was 0 mL. (2) There were 38 patients with knee osteoarthritis, including 30 females and 8 males. The mean age was (68.53±9.87) years old. The mean muscle content was (782 409.18±331 392.56) mL, the mean median was 689 105.66 mL, and the mean volume of effusion was (1 625.23±5 014.03) mL. The mean median was 178.72 mL. (3) There was no significant difference in muscle content between normal people and knee osteoarthritis patients. The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects, and the difference was significant (P < 0.05). (4) It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past. The more accuracy evaluation of knee osteoarthritis was necessary, and the image segmentation was processed more precisely in the future to improve the accuracy of the results.

Key words: knee osteoarthritis, deep learning, image recognition, intelligent segmentation, measurement analysis


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