# Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

arXiv: https://arxiv.org/abs/2307.03942 · HTML: https://mlsanity.com/paper/2307.03942/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [CRIS: CLIP-Driven Referring Image Segmentation](https://mlsanity.com/paper/2111.15174/) (2021)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2202.10185/) (2022)
- [Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports](https://mlsanity.com/paper/2110.03094/) (2021)

## Built on by (most cited first, 30 of 34)

- [MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization](https://mlsanity.com/paper/2507.02994/) (2025)
- [PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning](https://mlsanity.com/paper/2408.16769/) (2024)
- [SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance](https://mlsanity.com/paper/2409.04758/) (2024)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations](https://mlsanity.com/paper/2503.01019/) (2025)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues](https://mlsanity.com/paper/2406.19364/) (2024)
- [MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training](https://mlsanity.com/paper/2411.15576/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2504.01561/) (2025)
- [Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation](https://mlsanity.com/paper/2507.11055/) (2025)
- [Frequency-domain Multi-modal Fusion for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2509.19719/) (2025)
- [TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation](https://mlsanity.com/paper/2411.04595/) (2024)
- [Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation](https://mlsanity.com/paper/2503.11140/) (2025)
- [BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2503.23534/) (2025)
- [Text Embedded Swin-UMamba for DeepLesion Segmentation](https://mlsanity.com/paper/2508.06453/) (2025)
- [A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation](https://mlsanity.com/paper/2510.12482/) (2025)
- [Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2607.16327/) (2026)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [Medical Referring Image Segmentation via Next-Token Mask Prediction](https://mlsanity.com/paper/2511.05044/) (2025)
- [TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2511.05782/) (2025)
- [TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2512.21135/) (2025)
- [Spatial-aware Symmetric Alignment for Text-guided Medical Image Segmentation](https://mlsanity.com/paper/2512.22981/) (2025)
- [TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation](https://mlsanity.com/paper/2604.10912/) (2026)
- [Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment](https://mlsanity.com/paper/2605.15720/) (2026)
- [Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2606.27794/) (2026)
- [APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms](https://mlsanity.com/paper/2606.30577/) (2026)
- [Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation](https://mlsanity.com/paper/2607.09481/) (2026)
- [DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation](https://mlsanity.com/paper/2608.05683/) (2026)
- [MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation](https://mlsanity.com/paper/2608.13690/) (2026)

## Abstract (arXiv)

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to the uni-modal methods. Besides, our extended study reveals the flexibility of multi-modal methods in terms of the information granularity of text and demonstrates that multi-modal methods have a significant advantage over image-only methods in terms of the size of training data required.