# Test-Time Adaptation with Shape Moments for Image Segmentation

arXiv: https://arxiv.org/abs/2205.07983 · HTML: https://mlsanity.com/paper/2205.07983/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Parameter-free Online Test-time Adaptation](https://mlsanity.com/paper/2201.05718/) (2022)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Constrained-CNN losses for weakly supervised segmentation](https://mlsanity.com/paper/1805.04628/) (2018)
- [Source-Relaxed Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2005.03697/) (2020)
- [Constrained Deep Weak Supervision for Histopathology Image Segmentation](https://mlsanity.com/paper/1701.00794/) (2017)
- [Source-Free Domain Adaptation for Image Segmentation](https://mlsanity.com/paper/2108.03152/) (2021)
- [A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes](https://mlsanity.com/paper/1812.09953/) (2018)
- [Test-time Unsupervised Domain Adaptation](https://mlsanity.com/paper/2010.01926/) (2020)
- [Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening](https://mlsanity.com/paper/1611.01606/) (2016)
- [Incorporating prior knowledge in medical image segmentation: a survey](https://mlsanity.com/paper/1607.01092/) (2016)
- [Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!](https://mlsanity.com/paper/2105.00859/) (2021)

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Robust Test-Time Adaptation in Dynamic Scenarios](https://mlsanity.com/paper/2303.13899/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [DomainAdaptor: A Novel Approach to Test-time Adaptation](https://mlsanity.com/paper/2308.10297/) (2023)
- [Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning](https://mlsanity.com/paper/2307.09769/) (2023)
- [PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation](https://mlsanity.com/paper/2410.01573/) (2024)
- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation](https://mlsanity.com/paper/2407.20080/) (2024)
- [Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?](https://mlsanity.com/paper/2409.07960/) (2024)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025)
- [Single Image Test-Time Adaptation via Multi-View Co-Training](https://mlsanity.com/paper/2506.23705/) (2025)
- [Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.00676/) (2023)
- [Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation](https://mlsanity.com/paper/2310.11766/) (2023)
- [ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation](https://mlsanity.com/paper/2312.05407/) (2023)
- [Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation](https://mlsanity.com/paper/2508.09223/) (2025)
- [Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2512.01510/) (2025)
- [Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening](https://mlsanity.com/paper/2604.09062/) (2026)
- [Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2608.29923/) (2026)

## Abstract (arXiv)

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, and our code is available.