Transforming the Interactive Segmentation for Medical Imaging
This addresses the need for more accurate and flexible interactive segmentation tools in medical imaging, particularly for complex cases, though it appears incremental as it builds on existing interactive segmentation methods with a novel architecture.
The paper tackles the problem of refining automatic segmentation in medical imaging for challenging structures like cancer or small organs, proposing a Transformer-based architecture (TIS) that allows multi-category mask editing and achieves superior performance over state-of-the-art methods on three datasets.
The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.