RadioActive: 3D Radiological Interactive Segmentation Benchmark
Constantin Ulrich, Tassilo Wald, Emily Tempus, Maximilian, Rokuss, Paul F. Jaeger, Klaus Maier-Hein

TL;DR
RadioActive introduces a comprehensive benchmark for 3D radiological interactive segmentation, demonstrating that general-purpose models like SAM2 outperform specialized medical models with minimal interactions, and promotes reproducible evaluation.
Contribution
It provides a rigorous, extensible evaluation framework for 3D medical segmentation models and introduces advanced prompting techniques for fair comparison.
Findings
SAM2 outperforms specialized models with few interactions
The benchmark enables reliable and reproducible evaluation
General-purpose models surpass medical-specific approaches
Abstract
Effortless and precise segmentation with minimal clinician effort could greatly streamline clinical workflows. Recent interactive segmentation models, inspired by METAs Segment Anything, have made significant progress but face critical limitations in 3D radiology. These include impractical human interaction requirements such as slice-by-slice operations for 2D models on 3D data and a lack of iterative refinement. Prior studies have been hindered by inadequate evaluation protocols, resulting in unreliable performance assessments and inconsistent findings across studies. The RadioActive benchmark addresses these challenges by providing a rigorous and reproducible evaluation framework for interactive segmentation methods in clinically relevant scenarios. It features diverse datasets, a wide range of target structures, and the most impactful 2D and 3D interactive segmentation methods, all…
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Taxonomy
TopicsRadiology practices and education · Radiation Dose and Imaging
