Test Time Training for 4D Medical Image Interpolation

TL;DR

This paper introduces a test time training approach for 4D medical image interpolation that adapts models to new data distributions using self-supervision, improving performance without requiring labels.

Contribution

It proposes a novel test time training framework utilizing self-supervision for domain adaptation in 4D medical image interpolation, enhancing generalization across datasets.

Findings

Achieves higher PSNR values on Cardiac and 4D-Lung datasets.

Demonstrates significant performance improvements over baseline methods.

Provides a general template for domain adaptation in medical imaging.

Abstract

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.