­­A deep neural network framework of few-shot learning with domain adaptation for automatic meniscus segmentation in 3D Fast Spin Echo MRI

Automatic meniscus segmentation is highly desirable for quantitative analysis of knee joint diseases. As three-dimensional Fast Spin Echo (3D FSE) is a promising MR (magnetic resonance) imaging technique to evaluate the tissues of the knee joint. In this study, we explore meniscal segmentation on 3D FSE MRI. Manually annotating 3D knee images is challenging since it is time-consuming and requires clinical expertise. In this study, we propose a domain adaption-based few-shot learning method for meniscal segmentation on 3D FSE images using only one annotated MRI data. We demonstrate that the proposed method outperformed the fully supervised segmentation model.