PRS-Med: Position Reasoning Segmentation in Medical Imaging

TL;DR

PRS-Med introduces a clinical-first, position reasoning segmentation framework for medical imaging, leveraging a vision-language model and a large expert-validated dataset to improve accuracy and interpretability.

Contribution

It presents a novel, scalable approach combining a vision-language model with a new spatial reasoning dataset, enhancing medical image segmentation accuracy and clinical reliability.

Findings

Segmentation accuracy improved with up to +31.2% Dice score.

The PosMed dataset contains 116,000 expert-validated spatial QA pairs.

Outperforms state-of-the-art models in clinical reasoning and interpretability.

Abstract

Prompt-based medical image segmentation has rapidly emerged, yet existing methods rely on explicit prompts like bounding boxes and struggle to reason about the spatial relationships essential for clinical diagnosis. While general-domain models attempt complex coordinate regression, these approaches often lack the structured reliability required for medical applications. In this work, we introduce PRS-Med, a unified framework that adopts an elegant, clinical-first approach to position reasoning segmentation. By utilizing a medical vision-language model integrated with a segmentation decoder, PRS-Med mimics the structured "search patterns" used by radiologists to identify pathologies within specific anatomical zones. To support this robust reasoning, we present the Medical Position Reasoning Segmentation (PosMed) dataset, comprising 116,000 expert-validated, spatially grounded question-answer pairs across six imaging modalities. Unlike previous brittle attempts at spatial reasoning, PosMed leverages a scalable, deterministic pipeline validated by board-certified radiologists to ensure clinical accuracy. Extensive experiments demonstrate that our zone-based reasoning not only improves segmentation accuracy (mean Dice improvements up to +31.2\%) but also provides a high-confidence interpretability layer that outperforms state-of-the-art complex reasoning models. By prioritizing functional reliability over unnecessary technical complexity, PRS-Med offers a practical and scalable baseline for the next generation of intelligent medical assistants.