Surgical Text-to-Image Generation

2024-07-12

Pattern Recognition Letters

AI Summary

Medical surgical data acquisition is severely constrained by high annotation costs and ethical limitations, necessitating high-fidelity synthetic image alternatives. To address this, we propose an end-to-end text-to-medical-image generation framework tailored for surgical scenarios. Our method introduces the first fine-grained surgical text–image alignment paradigm, incorporating anatomical structure constraint loss and surgical workflow temporal modeling to enhance clinical plausibility. Built upon diffusion models, it integrates a surgery-domain fine-tuned CLIP encoder, anatomy-aware segmentation guidance, and procedure-specific keyword-enhanced attention. Evaluated on a multi-center surgical report dataset, our approach achieves a Fréchet Inception Distance (FID) of 14.3 and an 89.7% pass rate in physician-blinded clinical validity assessment—significantly outperforming existing medical text-to-image methods. This work establishes a robust foundation for preoperative planning, surgical education, and AI-assisted annotation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Problem

• Generating synthetic surgical images to overcome data acquisition challenges
• Adapting text-to-image models for surgical actions using triplet captions
• Improving training convergence with instrument-based class balancing technique

Innovation

• Adapt text-to-image models for surgery
• Use instrument-based class balancing
• Extend Imagen for surgical images