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Abstract #3198

Unsupervised Domain Adaptation via CycleGAN for knee joint Segmentation in MR Images

Siyue LI1, Sheheryar Khan2, Fan XIAO1, Shutian ZHAO1, Junru ZHONG1, Dόnal G. Cahill1, James F. Griffith1, and Weitian CHEN1
1The Chinese University of Hong Kong, Hong Kong, Hong Kong, 2The City University of Hong Kong, Hong Kong, Hong Kong


Knee joint tissues segmentation is necessary for quantitative analysis of musculoskeletal diseases like knee osteoarthritis. Three-dimensional Fast Spin Echo (3D FSE) imaging is a potential MRI technique for routine clinical knee imaging. Thus, segmentation based on 3D FSE has valuable clinical application. However, the conventional deep learning-based segmentation requires manually annotating 3D knee images which is time-consuming. In this work, we proposed a domain adaption-based unsupervised approach for cartilage and meniscus segmentation on 3D FSE images without the need for annotating images. We demonstrated that the proposed method improved the quality of segmentation.

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