Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, their 2D design and domain shift limitations make them ill-suited for 3D medical images. Current adaptations address some of these challenges but remain limited, either lacking volumetric awareness, offering restricted interactivity, or supporting only a small set of structures and modalities. Usability also remains a challenge, as current tools are rarely integrated into established imaging platforms and often rely on cumbersome web-based interfaces with restricted functionality. We introduce nnInteractive, the first comprehensive 3D interactive open-set segmentation method. It supports diverse prompts-including points, scribbles, boxes, and a novel lasso prompt-while leveraging intuitive 2D interactions to generate full 3D segmentations. Trained on 120+ diverse volumetric 3D datasets (CT, MRI, PET, 3D Microscopy, etc.), nnInteractive sets a new state-of-the-art in accuracy, adaptability, and usability. Crucially, it is the first method integrated into widely used image viewers (e.g., Napari, MITK), ensuring broad accessibility for real-world clinical and research applications. Extensive benchmarking demonstrates that nnInteractive far surpasses existing methods, setting a new standard for AI-driven interactive 3D segmentation. nnInteractive is publicly available:this https URL(Napari plugin),this https URL(MITK integration),this https URL(Python backend).
View on arXiv@article{isensee2025_2503.08373,
title={ nnInteractive: Redefining 3D Promptable Segmentation },
author={ Fabian Isensee and Maximilian Rokuss and Lars Krämer and Stefan Dinkelacker and Ashis Ravindran and Florian Stritzke and Benjamin Hamm and Tassilo Wald and Moritz Langenberg and Constantin Ulrich and Jonathan Deissler and Ralf Floca and Klaus Maier-Hein },
journal={arXiv preprint arXiv:2503.08373},
year={ 2025 }
}