PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging

• URL: http://arxiv.org/abs/2505.11872v2
• Date: Thu, 22 May 2025 08:00:20 GMT
• Title: PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging
• Authors: Quoc-Huy Trinh, Minh-Van Nguyen, Jung Peng, Ulas Bagci, Debesh Jha,
• Abstract summary: PRS-Med is a framework that integrates vision-language models with segmentation capabilities to generate both accurate segmentation masks and corresponding spatial reasoning outputs.<n> MMRS dataset provides diverse, spatially-grounded question-answer pairs to address the lack of position reasoning data in medical imaging.
• Score: 6.411386758550256
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent advancements in prompt-based medical image segmentation have enabled clinicians to identify tumors using simple input like bounding boxes or text prompts. However, existing methods face challenges when doctors need to interact through natural language or when position reasoning is required - understanding spatial relationships between anatomical structures and pathologies. We present PRS-Med, a framework that integrates vision-language models with segmentation capabilities to generate both accurate segmentation masks and corresponding spatial reasoning outputs. Additionally, we introduce the MMRS dataset (Multimodal Medical in Positional Reasoning Segmentation), which provides diverse, spatially-grounded question-answer pairs to address the lack of position reasoning data in medical imaging. PRS-Med demonstrates superior performance across six imaging modalities (CT, MRI, X-ray, ultrasound, endoscopy, RGB), significantly outperforming state-of-the-art methods in both segmentation accuracy and position reasoning. Our approach enables intuitive doctor-system interaction through natural language, facilitating more efficient diagnoses. Our dataset pipeline, model, and codebase will be released to foster further research in spatially-aware multimodal reasoning for medical applications.

Related papers

• Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation [56.52520416420957]
  We propose Multimodal Causal-Driven Representation Learning (MCDRL) to tackle domain generalization in medical image segmentation.<n>MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.
  arXiv  Detail & Related papers  (2025-08-07T03:41:41Z)
• RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features [3.0015555136149175]
  RadiomicsRetrieval is a 3D content-based retrieval framework for medical images.<n>Unlike existing 2D approaches, RadiomicsRetrieval fully exploits volumetric data to leverage richer spatial context in medical images.
  arXiv  Detail & Related papers  (2025-07-11T12:48:25Z)
• MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models [48.24824129683951]
  We introduce medical image reasoning segmentation, a novel task that aims to generate segmentation masks based on complex and implicit medical instructions.<n>To address this, we propose MedSeg-R, an end-to-end framework that leverages the reasoning abilities of MLLMs to interpret clinical questions.<n>It is built on two core components: 1) a global context understanding module that interprets images and comprehends complex medical instructions to generate multi-modal intermediate tokens, and 2) a pixel-level grounding module that decodes these tokens to produce precise segmentation masks.
  arXiv  Detail & Related papers  (2025-06-12T08:13:38Z)
• Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant [11.187690318227514]
  RCMed is a full-stack AI assistant that improves multimodal alignment in both input and output.<n>It achieves state-of-the-art precision in contextualizing irregular lesions and subtle anatomical boundaries.
  arXiv  Detail & Related papers  (2025-05-06T10:00:08Z)
• MRGen: Segmentation Data Engine For Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically significant imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize training data, to train segmentation models for underrepresented modalities.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• LIMIS: Towards Language-based Interactive Medical Image Segmentation [58.553786162527686]
  LIMIS is the first purely language-based interactive medical image segmentation model. We adapt Grounded SAM to the medical domain and design a language-based model interaction strategy. We evaluate LIMIS on three publicly available medical datasets in terms of performance and usability.
  arXiv  Detail & Related papers  (2024-10-22T12:13:47Z)
• Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images [30.673958586581904]
  We introduce FLanS, a novel medical image segmentation model that handles various free-form text prompts. FLanS is trained on a large-scale dataset of over 100k medical images from 7 public datasets.
  arXiv  Detail & Related papers  (2024-10-02T16:34:32Z)
• D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions [8.50767187405446]
  We propose D-Rax -- a domain-specific, conversational, radiologic assistance tool. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations.
  arXiv  Detail & Related papers  (2024-07-02T18:43:10Z)
• Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training [99.2891802841936]
  We introduce the Med-ST framework for fine-grained spatial and temporal modeling. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR)
  arXiv  Detail & Related papers  (2024-05-30T03:15:09Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models [0.8878802873945023]
  This study introduces the first systematic study on transferring Vision-Language Models to 2D medical images. Although VLSMs show competitive performance compared to image-only models for segmentation, not all VLSMs utilize the additional information from language prompts.
  arXiv  Detail & Related papers  (2023-08-15T11:28:21Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [72.8965643836841]
  We introduce XrayGPT, a novel conversational medical vision-language model.<n>It can analyze and answer open-ended questions about chest radiographs.<n>We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.