论文标题
转换用于医学成像的互动分割
Transforming the Interactive Segmentation for Medical Imaging
论文作者
论文摘要
本文的目的是互动地完善自动细分，这些分割是由于可用注释的稀缺性或问题本身的难度性质，例如，在癌症或小型器官方面的困难性质，落后于人类绩效的具有挑战性的结构。具体而言，我们建议一种基于变压器的新型体系结构用于交互式分割（TIS），该体系结构将精炼任务视为将与最终用户提供的点击相似的像素分组的过程。我们提出的架构由变压器解码器变体组成，该变体自然可以实现特征与注意机制的比较。与现有方法相反，我们提出的TIS不仅限于二进制细分，因此允许用户为任意数量的类别编辑口罩。为了验证所提出的方法，我们对三个具有挑战性的数据集进行了广泛的实验，并证明了比现有最新方法的卓越性能。项目页面为：https：//wtliu7.github.io/tis/。
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/.
