ArticleNPJ digital medicine2025
Large-vocabulary segmentation for medical images with text prompts.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- NeuroLangSeg: Language-Guided Subcortical Segmentation with Pseudo-Supervision and Anatomical-Linguistic Validation.Proceedings of machine learning research · 2026Article
- MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Adaptive patch sampling and location-aware reasoning for whole body PET-CT multi-organ segmentation.Scientific reports · 2026Article
- SHIELD: An AI Framework for Skeletal Health Intelligence and Early Lesion Detection to Improve Orthopedic Referrals.Journal of medical systems · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- Article
- Development of a large-scale grounded vision language dataset for chest CT analysis.Scientific data · 2025Article
- Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.Korean journal of radiology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This paper aims to build a model that can Segment Anything in 3D medical images, driven by medical terminologies as Text prompts, termed as SAT. Our main contributions are three-fold: (i) We construct the first multimodal knowledge tree on human anatomy, including 6502 anatomical terminologies; Then, we build the largest and most comprehensive segmentation dataset for training, collecting over 22K 3D scans from 72 datasets, across 497 classes, with careful standardization on both image and label space; (ii) We propose to inject medical knowledge into a text encoder via contrastive learning and formulate a large-vocabulary segmentation model that can be prompted by medical terminologies in text form. (iii) We train SAT-Nano (110M parameters) and SAT-Pro (447M parameters). SAT-Pro achieves comparable performance to 72 nnU-Nets-the strongest specialist models trained on each dataset (over 2.2B parameters combined)-over 497 categories. Compared with the interactive approach MedSAM, SAT-Pro consistently outperforms across all 7 human body regions with +7.1% average Dice Similarity Coefficient (DSC) improvement, while showing enhanced scalability and robustness. On 2 external (cross-center) datasets, SAT-Pro achieves higher performance than all baselines (+3.7% average DSC), demonstrating superior generalization ability.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.