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arXiv Machine Learning By Fabian Isensee, Constantin Ulrich Harsy, Maximilian Rokuss, Benjamin Hamm, Philipp Vollmuth, Klaus H. Maier-Hein

nnInteractive Agent: General-Purpose AI Agents as 3D Image Annotators

Read the original on arXiv Machine Learning →

The paper introduces nnInteractive Agent, an open‑source system that lets general‑purpose AI agents perform 3D image annotation tasks by interacting with the nnInteractive segmentation model. Users issue plain‑language requests, and the agent automatically identifies targets, places prompts, evaluates and corrects the resulting volumetric masks, and can even generate code for additional processing. On a diverse benchmark of CT, MRI, PET, micro‑CT, and insect scans, agents such as Claude Code and Codex achieve mean Dice scores of 0.81–0.84 from the request alone, matching near‑oracle performance when given target locations.

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