Self-supervised Test-Time Adaptation for Medical Image Segmentation

The performance of convolutional neural networks (CNNs) often drop when they encounter a domain shift. Recently, unsupervised domain adaptation (UDA) and domain generalization (DG) techniques have been proposed to solve this problem. However, access to source domain data is required for UDA and DG approaches, which may not always be available in practice due to data privacy. In this paper, we propose a novel test-time adaptation framework for volumetric medical image segmentation without any source domain data for adaptation and target domain data for offline training. Specifically, our proposed framework only needs pre-trained CNNs in the source domain, and the target image itself. Our method aligns the target image on both image and latent feature levels to source domain during the test-time. There are three parts in our proposed framework: (1) multi-task segmentation network (Seg), (2) autoencorders (AEs) and (3) translation network (T). Seg and AEs are pre-trained with source domain data. At test-time, the weights of these pre-trained CNNs (decoders of Seg and AEs) are fixed, and T is trained to align the target image to source domain at imagelevel by the autoencoders which optimize the similarity between input and reconstructed output. The encoder of Seg is also updated to increase the domain generalizability of the model towards the source domain at the feature level with self-supervised tasks. We evaluate our method on healthy controls, adult Huntington's disease (HD) patients and pediatric Aicardi Gouti`eres Syndrome (AGS) patients, with different scanners and MRI protocols. The results indicate that our proposed method improves the performance of CNNs in the presence of domain shift at test-time.