INTRABENCH: Interactive Radiological Benchmark
Keywords: interactive, prompt, segmentation, medical image computing, medical, human in the loop, sam, medical image segmentation
TL;DR: We present the first easy-to-use, extendable, interactive medical image segmentation benchmark together with a new human-centric category of prompt mechanisms
Abstract: 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.
Primary Area: datasets and benchmarks
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Submission Number: 7180
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