Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)

ABSTRACT: Vision transformers efficiently model long-range context and thus have demonstrated impressive accuracy gains in several image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a self-distillation learning with masked image modeling method to perform SSL for vision transformers (SMIT) applied to 3D multi-organ segmentation from CT and MRI. Our contribution combines a dense pixel-wise regression pretext task performed within masked patches called masked image prediction with masked patch token distillation to pre-train vision transformers. Our app

SUBMITTER: Jiang J 

PROVIDER: S-EPMC9714226 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature