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.

本研究致力于解决当前交互式分割方法在实际临床应用中面临的重大局限，如不切实际的人机交互要求和缺乏迭代细化等。通过提供一个全面且可重复的评估框架，IntRaBench克服了这些挑战，使得针对现实临床场景的分割方法能够获得公平的比较和评估。该框架的开源特性鼓励研究社区不断整合新模型和策略，从而推动3D医学影像中交互式分割模型的持续和透明评估。

INTRABENCH：交互式放射学基准