Test-Time Adaptation with Shape Moments for Image Segmentation

• Published in: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 Vol. 13434; pp. 736 - 745
• Main Authors: Bateson, Mathilde, Lombaert, Herve, Ben Ayed, Ismail
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Deep networks; Entropy minimization; Segmentation; Shape moments; Test-time adaptation
• ISBN: 9783031164392; 3031164393
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-16440-8_70

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few (or even a single) subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a shape-guided entropy minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization’s scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network. The code is publicly available (https://github.com/mathilde-b/TTA).