Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering

• URL: http://arxiv.org/abs/2503.13806v1
• Date: Tue, 18 Mar 2025 01:35:34 GMT
• Title: Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering
• Authors: Wenjie Zhang, Ziyang Zhang, Mengnan He, Jiancheng Ye,
• Abstract summary: Existing medical image segmentation methods rely on uni-modal visual inputs, such as images or videos, requiring labor-intensive manual annotations.<n>Medical imaging techniques capture multiple intertwined organs within a single scan, further complicating segmentation accuracy.<n>To address these challenges, MedSAM was developed to enhance segmentation accuracy by integrating image features with user-provided prompts.
• Score: 17.273290949721975
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate segmentation is essential for effective treatment planning and disease monitoring. Existing medical image segmentation methods predominantly rely on uni-modal visual inputs, such as images or videos, requiring labor-intensive manual annotations. Additionally, medical imaging techniques capture multiple intertwined organs within a single scan, further complicating segmentation accuracy. To address these challenges, MedSAM, a large-scale medical segmentation model based on the Segment Anything Model (SAM), was developed to enhance segmentation accuracy by integrating image features with user-provided prompts. While MedSAM has demonstrated strong performance across various medical segmentation tasks, it primarily relies on geometric prompts (e.g., points and bounding boxes) and lacks support for text-based prompts, which could help specify subtle or ambiguous anatomical structures. To overcome these limitations, we propose the Organ-aware Multi-scale Text-guided Medical Image Segmentation Model (OMT-SAM) for multi-organ segmentation. Our approach introduces CLIP encoders as a novel image-text prompt encoder, operating with the geometric prompt encoder to provide informative contextual guidance. We pair descriptive textual prompts with corresponding images, processing them through pre-trained CLIP encoders and a cross-attention mechanism to generate fused image-text embeddings. Additionally, we extract multi-scale visual features from MedSAM, capturing fine-grained anatomical details at different levels of granularity. We evaluate OMT-SAM on the FLARE 2021 dataset, benchmarking its performance against existing segmentation methods. Empirical results demonstrate that OMT-SAM achieves a mean Dice Similarity Coefficient of 0.937, outperforming MedSAM (0.893) and other segmentation models, highlighting its superior capability in handling complex medical image segmentation tasks.

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