Current interactive segmentation approaches, inspired by the success of META's Segment Anything model, have achieved notable advancements, however, they come with substantial limitations that hinder their practical application in real clinical scenarios. These include unrealistic human interaction requirements, such as slice-by-slice operations for 2D models on 3D data, a lack of iterative refinement, and insufficient evaluation experiments. These shortcomings prevent accurate assessment of model performance and lead to inconsistent outcomes across studies. IntRaBench overcomes these challenges by offering a comprehensive and reproducible framework for evaluating interactive segmentation methods in realistic, clinically relevant scenarios. It includes diverse datasets, target structures, and segmentation models, and provides a flexible codebase that allows seamless integration of new models and prompting strategies. Additionally, we introduce advanced techniques to minimize clinician interaction, ensuring fair comparisons between 2D and 3D models. By open-sourcing IntRaBench, we invite the research community to integrate their models and prompting techniques, ensuring continuous and transparent evaluation of interactive segmentation models in 3D medical imaging.

翻译：当前基于META的Segment Anything模型成功启发的交互式分割方法虽已取得显著进展，但仍存在严重局限，阻碍了其在真实临床场景中的实际应用。这些局限包括不切实际的人机交互需求（例如在三维数据上对二维模型进行逐切片操作）、缺乏迭代优化机制以及评估实验不足。这些缺陷导致模型性能无法被准确评估，并造成不同研究间结果不一致。IntRaBench通过提供一个全面且可复现的框架来克服这些挑战，该框架可在符合临床实际的相关场景中评估交互式分割方法。它包含多样化数据集、目标结构与分割模型，并提供灵活的代码库，支持新模型与提示策略的无缝集成。此外，我们引入了先进技术以最小化临床医生的交互操作，确保二维与三维模型间的公平比较。通过开源IntRaBench，我们邀请研究社区集成各自的模型与提示技术，以保障三维医学影像中交互式分割模型持续且透明的评估。