# VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2511.11450 · HTML: https://mlsanity.com/paper/2511.11450/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 14 Nov 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Built on by (most cited first, 19 of 19)

- [Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data](https://mlsanity.com/paper/2511.21926/) (2025)
- [CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification](https://mlsanity.com/paper/2605.09002/) (2026)
- [TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT](https://mlsanity.com/paper/2605.16572/) (2026)
- [Open-Ended CT Volume Segmentation with Weak Supervision from Language](https://mlsanity.com/paper/2607.25860/) (2026)
- [TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy](https://mlsanity.com/paper/2603.09217/) (2026)
- [Hierarchical Text-Guided Brain Tumor Segmentation via Sub-Region-Aware Prompts](https://mlsanity.com/paper/2603.21083/) (2026)
- [DGRNet: Disagreement-Guided Refinement for Uncertainty-Aware Brain Tumor Segmentation](https://mlsanity.com/paper/2603.21086/) (2026)
- [Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking](https://mlsanity.com/paper/2605.23118/) (2026)
- [Decouple and Reason: Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT](https://mlsanity.com/paper/2607.12602/) (2026)
- [UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation](https://mlsanity.com/paper/2607.12896/) (2026)
- [MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation](https://mlsanity.com/paper/2608.00195/) (2026)
- [Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model](https://mlsanity.com/paper/2608.05389/) (2026)
- [EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation](https://mlsanity.com/paper/2608.07299/) (2026)
- [NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation](https://mlsanity.com/paper/2608.09636/) (2026)
- [MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation](https://mlsanity.com/paper/2608.13690/) (2026)
- [Instance-Guided Report Anchoring for Text-Free 3D Abnormality Segmentation in Chest CT](https://mlsanity.com/paper/2609.00447/) (2026)
- [MedVA: An End-to-End Neuro-Symbolic Agentic System for Medical Volume Visualization](https://mlsanity.com/paper/2609.14874/) (2026)
- [SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images](https://mlsanity.com/paper/2609.25743/) (2026)
- [Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset](https://mlsanity.com/paper/2609.36436/) (2026)

## Abstract (arXiv)

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 over 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://www.github.com/MIC-DKFZ/VoxTell