Context-Semantic Guided Diffusion for Medical Image Synthesis
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Solution Overview
Problem
Existing medical imaging systems face challenges in implementing AI and ML due to the demand for significant amounts of curated data, limited availability of diverse and unbiased training data, and limitations in generating synthetic images with controlled anatomical structure and textural features.
Innovation Solution
A three-stage AI solution using a conditional latent diffusion model with semantic and context guidance to generate synthetic images, incorporating a mask generation network, context selection network, and image generation network to control anatomical geometry and textural features, enhancing data diversity and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing medical imaging systems use AI and ML techniques, then diagnostic accuracy can be improved, but the demand for significant amounts of curated training data increases
Solution Approach 1:
The patent uses generative adversarial networks (GANs) to create synthetic copies of medical images that replicate the statistical properties and visual characteristics of real medical images. These synthetic images serve as artificial training data, allowing AI models to be trained without requiring additional real patient data, thus resolving the contradiction between improving diagnostic accuracy and reducing training data volume requirements
Solution Approach 2:
The patent modifies the parameter space by introducing controlled variations in image generation parameters such as noise levels, transformation types, and augmentation strengths. By systematically varying these parameters, the system generates diverse synthetic images that maintain medical realism while providing sufficient training data volume, thereby improving diagnostic accuracy without proportionally increasing real data requirements
2Adaptability or versatility
If synthetic images are generated to augment training data, then data diversity can be improved, but control over anatomical structure and textural features becomes limited
Solution Approach 1:
The patent introduces semantic masks as intermediary elements that bridge the gap between random noise input and controlled image output. These masks encode specific anatomical structures and guide the GAN generation process, ensuring that synthetic images maintain precise control over anatomical features while still achieving diverse textural variations through the adversarial training mechanism
Solution Approach 2:
The patent segments the image generation process into distinct components: semantic mask generation, noise injection, and adversarial refinement. This segmentation allows independent control over anatomical structure (through masks) and textural features (through adversarial training), simultaneously achieving both data diversity and manufacturing precision in the generated synthetic images
3Measurement precision
If more curated medical images are collected for training, then AI model accuracy can be enhanced, but the time and resources required for data curation increase
Solution Approach 1:
The patent creates synthetic copies of medical images using GANs that inherit the statistical properties and diagnostic features of real images. These copies serve as ready-to-use training data without requiring manual curation, annotation, or quality verification, thereby enhancing AI model accuracy while eliminating the time-consuming data curation process
Solution Approach 2:
The patent implements self-service data generation where the system automatically produces training data without human intervention. The GAN framework autonomously generates synthetic images with appropriate medical characteristics, eliminating the need for manual data collection, screening, and preparation, thus improving AI model accuracy while reducing data curation time to zero
Data Source
AI summary
Systems and methods for providing a context-semantic guided diffusion approach in medical image generation are described herein. In one example, a system includes a processing circuit having a processor coupled to a memory device. The memory device stores instructions thereon that, when executed, cause the processing circuit to perform operations including generating a semantic mask representing an anatomical structure; identifying a contextual image having the at least one textural feature; and applying the semantic mask and the contextual image to an artificial intelligence model. The artificial intelligence model is configured to generate a synthetic image having the anatomical structure and the at least one textural feature.