S$^3$-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation
This work addresses the challenge of improving segmentation accuracy for biomedical images, which is incremental as it builds on existing test-time augmentation methods.
The paper tackled the problem of test-time augmentation in biomedical image segmentation by proposing S^3-TTA, a framework that selects suitable image scale and style for each test image, resulting in improvements of 3.4% and 1.3% over prior art on cell and lung segmentation benchmarks.
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.