VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation
Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff, Fabian Isensee, Benjamin Hamm, Constantin Ulrich, Sebastian Regnery, Lukas Bauer
Abstract
We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences, to 3D masks. Trained on 62K+ CT, MRI, and PET volumes spanning 1K+ anatomical and pathological classes, VoxTell uses multi-stage vision-language fusion across decoder layers to align textual and visual features at multiple scales. It achieves state-of-the-art zero-shot performance across modalities on unseen datasets, excelling on familiar concepts while generalizing to related unseen classes. Extensive experiments further demonstrate strong cross-modality transfer, robustness to linguistic variations and clinical language, as well as accurate instance-specific segmentation from real-world text. Code is available at: https://github.com/MIC-DKFZ/VoxTell
BibTeX
@inproceedings{cvpr2026_voxtellfreetextp,
title = {VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation},
author = {Maximilian Rokuss and Moritz Langenberg and Yannick Kirchhoff and Fabian Isensee and Benjamin Hamm and Constantin Ulrich and Sebastian Regnery and Lukas Bauer and Efthimios Katsigiannopulos and Tobias Norajitra and Klaus Maier-Hein},
booktitle = {CVPR 2026},
year = {2026}
}