In this paper, we propose a novel approach to enhance medical image segmentation during test time. Instead of employing hand-crafted transforms or functions on the input test image to create multiple views for test-time augmentation, we advocate for the utilization of an advanced domain-fine-tuned generative model (GM), e.g., stable diffusion (SD), for test-time augmentation. Given that the GM has been trained to comprehend and encapsulate comprehensive domain data knowledge, it is superior than segmentation models in terms of representing the data characteristics and distribution. Hence, by integrating the GM into test-time augmentation, we can effectively generate multiple views of a given test sample, aligning with the content and appearance characteristics of the sample and the related local data distribution. This approach renders the augmentation process more adaptable and resilient compared to conventional handcrafted transforms. Comprehensive experiments conducted across three medical image segmentation tasks (nine datasets) demonstrate the efficacy and versatility of the proposed TTGA in enhancing segmentation outcomes. Moreover, TTGA significantly improves pixel-wise error estimation, thereby facilitating the deployment of a more reliable segmentation system. Code will be released at: https://github.com/maxiao0234/TTGA.

翻译：本文提出了一种在测试阶段增强医学图像分割性能的新方法。与采用手工设计的变换或函数对输入测试图像生成多视图进行测试时增强的传统方法不同，我们主张利用先进的领域微调生成模型（例如稳定扩散模型）进行测试时增强。鉴于生成模型经过训练能够理解并封装全面的领域数据知识，其在表征数据特征与分布方面优于分割模型。因此，通过将生成模型集成至测试时增强流程，我们能够有效生成给定测试样本的多个视图，这些视图与样本的内容及外观特征以及相关局部数据分布保持一致。相较于传统手工设计的变换方法，本方法使增强过程更具适应性与鲁棒性。在三个医学图像分割任务（涵盖九个数据集）上进行的全面实验证明了所提出的测试时生成增强方法在提升分割效果方面的有效性与普适性。此外，该方法显著改善了像素级误差估计，从而有助于部署更可靠的分割系统。代码将在以下地址发布：https://github.com/maxiao0234/TTGA。