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/.
翻译:本文的目的是互动地完善关于人类性能落后的具有挑战性的结构的自动分割,其原因有二,一是缺乏可用的说明,二是问题本身的难度,例如癌症或小器官的分割。具体地说,我们提议建立一个新型的基于变异器的交互式分割结构(TIS),将完善任务作为将具有与最终用户提供的点击功能相似特征的像素分组的程序。我们的拟议结构由变异器Decoder构成,自然地与关注机制进行特征比较。与现有的方法不同,我们提议的TIS不限于二元分割,而允许用户为任意的类别编辑面罩。为了验证拟议的方法,我们在三个具有挑战性的数据集上进行了广泛的实验,并展示了现有最新技术方法的优异性表现。项目网页是:https://wtliu7.github.io/tis/。