Article

Language-Guided Segmentation of Medical Images: A Review of Foundation Models

2026-06-22

Abstract excerpt

Vision-language foundation models have transformed medical image segmentation over the past three years. These models pair large image encoders with text prompts, so a single model can segment many anatomical structures, lesion types, and imaging modalities through natural language. This survey reviews vision-language foundation models designed for medical image segmentation. We describe the technical background f...

Topics

Open a Topic to create a Post that cites this publication.

Radiomics and Machine Learning in Medical Imaging

Identifiers and source

Literature Corpus work: 213cae21-24f8-5423-812f-c57449a3eb44
DOI: 10.20944/preprints202606.1606.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Language-Guided Segmentation of Medical Images: A Review of Foundation ModelsDOI 10.20944/preprints202606.1606.v1Read

A Decade of Deep Learning-based Biomedical Image SegmentationSemantic proximity Read

Merlin: A Vision Language Foundation Model for 3D Computed TomographySemantic proximity Read

A Survey of Generalization and Adaptation in Medical Imaging Foundation ModelsSemantic proximity Read

Generative AI Enables Medical Image Segmentation in Ultra Low-Data RegimesSemantic proximity Read

UniMRISegNet: Universal 3D Network for Various Organs and Cancers Segmentation on Multi-Sequence MRISemantic proximity Read

OrganSegBench: A Comprehensive Multi-Organ Benchmark for Segmentation Foundation Models with a Practical Synergy Pathway to Clinical ApplicationSemantic proximity Read

An Empirical Evaluation of Low-Rank Adapted Vision–Language Models for Radiology Medical Image CaptioningSemantic proximity Read

Synthetic MRI Pretraining for Medical Imaging TasksSemantic proximity Read

Generative AI Enables Medical Image Segmentation in Ultra Low-Data RegimesSemantic proximity Read

Triad: Vision Foundation Model for 3D Magnetic Resonance ImagingSemantic proximity Read

PolySAM-Lite: Parameter-efficient adaptation of the Segment Anything Model for colorectal polyp segmentationSemantic proximity Read

DHR-Net: An Ultra-Lightweight U-Net Based on Efficient Convolutional Attention for Medical Image SegmentationSemantic proximity Read