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 3D radiological scenarios. These include unrealistic human interaction requirements, such as slice-by-slice operations for 2D models on 3D data, a lack of iterative interactive refinement, and insufficient evaluation experiments. These shortcomings prevent accurate assessment of model performance and lead to inconsistent outcomes across studies. The RadioActive benchmark overcomes these challenges by offering a comprehensive and reproducible evaluation of interactive segmentation methods in realistic, clinically relevant scenarios. It includes diverse datasets, target structures, and interactive segmentation methods, and provides a flexible, extendable codebase that allows seamless integration of new models and prompting strategies. We also introduce advanced prompting techniques to enable 2D models on 3D data by reducing the needed number of interaction steps, enabling a fair comparison. We show that surprisingly the performance of slice-wise prompted approaches can match native 3D methods, despite the domain gap. Our findings challenge the current literature and highlight that models not specifically trained on medical data can outperform the current specialized medical methods. By open-sourcing RadioActive, 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：当前交互式分割方法受META的Segment Anything模型成功启发已取得显著进展，然而其在三维放射学场景的实际应用中仍存在诸多局限。这些限制包括不切实际的人机交互需求（如在三维数据上对二维模型进行逐切片操作）、缺乏迭代式交互优化机制以及评估实验设计不足。这些缺陷阻碍了对模型性能的准确评估，并导致不同研究间结果不一致。RadioActive基准通过提供全面且可复现的评估框架，在真实临床相关场景中克服了上述挑战。该基准包含多样化数据集、目标结构与交互式分割方法，并提供灵活可扩展的代码库，支持新模型与提示策略的无缝集成。我们还引入先进的提示技术，通过减少交互步骤使二维模型能够处理三维数据，从而实现公平比较。研究表明，尽管存在领域差异，采用切片级提示方法的性能竟能与原生三维方法相媲美。我们的发现对现有文献提出挑战，并揭示未专门针对医学数据训练的模型可能超越当前专业医学方法。通过开源RadioActive，我们邀请研究社区集成其模型与提示技术，确保三维医学影像交互式分割模型能获得持续透明的评估。