Efficient Deformable Convolutional Prompt for Continual Test-Time Adaptation in Medical Image Segmentation

The domain gap resulting from mismatches in acquisition details like protocol and scanner between training and test data hinders the deployment of the trained model in clinical practice. To address this issue, Continual test-time adaptation (CTTA) has been proposed to adapt the source model to continually changing unlabeled domains without accessing the source data. Existing methods learn an image-level visual prompt for target domains and inject the trainable prompt into the input space. However, they either combine the input with a prompt of equal scale or determine the prompt injection position through complex strategies such as uncertainty estimation or Fourier Transform. These approaches substantially increase the number of trainable parameters and computational burden, especially in high-dimensional medical imaging data. To overcome these challenges, we propose the Efficient Deformable Convolutional Prompt (EDCP), which leverages the inductive bias of convolution to reduce trainable parameters compared to standard prompts. We further enhance convolution by making it deformable, addressing fine-grained domain shifts at the pixel level through an offset branch. To improve training efficiency and balance parameters between the convolution and offset branches, we decompose the offset transformation into two parts, storing one in an offset bank that also serves as a domain indicator. This bank accelerates training by skipping test images similar to those already stored. Prompt updates are guided by layer-wise alignment of source-target statistics without unfreezing batch normalization layers. Extensive experiments demonstrate the superiority of our method in 2D and 3D medical image segmentation tasks.