Deep-learning models have been successful in biomedical image segmentation. To generalize for real-world deployment, test-time augmentation (TTA) methods are often used to transform the test image into different versions that are hopefully closer to the training domain. Unfortunately, due to the vast diversity of instance scale and image styles, many augmented test images produce undesirable results, thus lowering the overall performance. This work proposes a new TTA framework, S$^3$-TTA, which selects the suitable image scale and style for each test image based on a transformation consistency metric. In addition, S$^3$-TTA constructs an end-to-end augmentation-segmentation joint-training pipeline to ensure a task-oriented augmentation. On public benchmarks for cell and lung segmentation, S$^3$-TTA demonstrates improvements over the prior art by 3.4% and 1.3%, respectively, by simply augmenting the input data in testing phase.

翻译：深度学习模型在生物医学图像分割中已取得显著成功。为提升实际部署中的泛化能力，测试时增强方法通常将原始测试图像变换为更接近训练域的不同版本。然而，由于实例尺度与图像风格的巨大差异，许多增强后的测试图像会产生不良结果，从而降低整体性能。本文提出一种新的测试时增强框架S$^3$-TTA，该框架基于变换一致性度量，为每张测试图像选择合适的尺度和风格。此外，S$^3$-TTA构建了端到端的增强-分割联合训练流水线，以确保任务导向的增强效果。在细胞分割与肺部分割公开基准上的实验表明，S$^3$-TTA仅通过在测试阶段对输入数据进行增强，就分别比现有技术提升3.4%和1.3%的性能。