Supervoxels-based Self-supervised Few-shot 3D Medical Image Segmentation via Multiple Features Transfer

In recent years, medical image segmentation technology has made great progress. However, the annotated data of 3D medical images is relatively small, and although fewshot segmentation can solve this problem, there are still many challenges. Too few samples of support images may lead to the fact that it is difficult to fully represent 3D medical images, especially the important 3D spatial information, and the global correlations between support and query images are not fully utilized. In this paper, we propose a novel few-shot 3D medical image segmentation pipeline framework, SMFT-Net, which can efficiently accomplish the 3D medical image segmentation task using only one labeled sample. Specifically, we proposed pretrained feature transfer module (PFTM) and bidirectional feature transfer module (BFTM) for multiple feature transfer of 3D medical image. PFTM can be used for 3D feature transfer to ensure that the 3D spatial information of medical images is preserved. And BFTM can perform bi-directional feature transfer between the query image and the support image to eliminate extraneous information from the surrounding pixels. Extensive experiments on four medical image datasets demonstrate that our method outperforms the state-of-the-art methods.