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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