[논문]Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

Kim, Joo Young; Ro, Kyunghan; You, Sungmin; Nam, Bo Rum; Yook, Sunhyun; Park, Hee Seol; Yoo, Jae Chul; Park, Eunkyoung; Cho, Kyeongwon; Cho, Baek Hwan; Kim, In Young

doi:10.1016/j.cmpb.2019.105063

Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

Computer methods and programs in biomedicine, v.182, 2019년, pp.105063 -

Kim, Joo Young (Department of Biomedical Engineering, Hanyang University) , Ro, Kyunghan (Bonbridge hospital) , You, Sungmin (Department of Biomedical Engineering, Hanyang University) , Nam, Bo Rum (Department of Biomedical Engineering, Hanyang University) , Yook, Sunhyun (Department of Biomedical Engineering, Hanyang University) , Park, Hee Seol (Samsung Medical Center, Sungkyunkwan University School of Medicine) , Yoo, Jae Chul (Samsung Medical Center, Sungkyunkwan University School of Medicine) , Park, Eunkyoung (Biomedical Engineering Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine) , Cho, Kyeongwon (Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine) , Cho, Baek Hwan (Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine South Korea<) , Kim, In Young

Abstract ▼ AI-Helper

Abstract Background and objective Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these diseases, but subjective intervention of the diagnosis is an obstacle in accurately detecting the correct region. Methods Therefore, in this paper, we propose a fully convolutional deep learning algorithm to quantitatively detect the fossa and muscle region by measuring the occupation ratio of supraspinatus in the supraspinous fossa. In the development and performance evaluation of the algorithm, 240 patients MRI dataset with various disease severities were included. Results As a result, the pixel-wise accuracy of the developed algorithm is 0.9984 ± 0.073 in the fossa region and 0.9988 ± 0.065 in the muscle region. The dice coefficient is 0.9718 ± 0.012 in the fossa region and 0.9463 ± 0.047 in the muscle region. Conclusions We expect that the proposed convolutional neural network can improve the efficiency and objectiveness of diagnosis by quantifying the index used in the orthopedic rotator cuff tear. Highlights A deep learning-based algorithm for detecting the region of interest in medical images. Objective accuracy of developed algorithm was evaluated through 10-fold cross validation of 240 patient data. Developed algorithm pixel accuracy was 99.86% and dice coefficient was 0.9590 on average. This algorithm can be used in statistical analysis of clinical surgery and diagnosis area to improve efficiency and patient satisfaction in orthopedic and various medical field.

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참고문헌 (26)

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Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

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Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning

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