Test Time Training for 4D Medical Image Interpolation
This work addresses generalization issues in clinical applications for medical imaging, offering a domain adaptation template for fields like segmentation and registration.
The paper tackles the problem of distribution shifts in 4D medical image interpolation by proposing a test time training framework that uses self-supervision to adapt models without labels, achieving peak signal-to-noise ratio values of 33.73dB on Cardiac and 34.02dB on 4D-Lung datasets.
4D medical image interpolation is essential for improving temporal resolution and diagnostic precision in clinical applications. Previous works ignore the problem of distribution shifts, resulting in poor generalization under different distribution. A natural solution would be to adapt the model to a new test distribution, but this cannot be done if the test input comes without a ground truth label. In this paper, we propose a novel test time training framework which uses self-supervision to adapt the model to a new distribution without requiring any labels. Indeed, before performing frame interpolation on each test video, the model is trained on the same instance using a self-supervised task, such as rotation prediction or image reconstruction. We conduct experiments on two publicly available 4D medical image interpolation datasets, Cardiac and 4D-Lung. The experimental results show that the proposed method achieves significant performance across various evaluation metrics on both datasets. It achieves higher peak signal-to-noise ratio values, 33.73dB on Cardiac and 34.02dB on 4D-Lung. Our method not only advances 4D medical image interpolation but also provides a template for domain adaptation in other fields such as image segmentation and image registration.