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Abstract #4046

Development of Deep Learning based Cartilage Segmentation at 3D knee MRI for the use of Biomarker of Osteoarthritis

Jinwoo Han1, Suk-Joo Hong1, Zepa Yang1, Woo Young Kang1, Yoonmi Choi1, Chang Ho Kang2, Kyung-sik Ahn2, Baek Hyun Kim3, and Euddeum Shim3
1Radiology, Korea University Guro Hospital, KUGH-MIDC, Seoul, Korea, Republic of, 2Korea University Anam Hospital, Seoul, Korea, Republic of, 3Korea University Ansan Hospital, Ansan, Korea, Republic of

Cartilage loss is fundamental pathology of knee osteoarthritis (OA). Quantitative analysis of cartilage thickness and volume is very time consuming by manual measurement. We proposed development of deep learning based cartilage segmentation at three dimensional knee magnetic resonance images, which can measure thickness and volume of knee joint cartilage, automatically and accurately. To evaluate the performance, we used Dice Similarity Coefficient (DSC) respect to the manual segmentation, and visual inspection. The accuracy DSC values were higher than 0.9. We expect deep learning program can be useful in future study for knee joint osteoarthritis.

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