  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.
