3D Diffusion Image Patches for Low-Annotation Organ Segmentation
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Solution Overview
Problem
Existing automated segmentation models require a large volume of manually contoured data for training, which is challenging and time-consuming, especially when working with different types of medical imaging modalities like CT, T1w MR, T2w MR, and ultrasound, leading to inefficiencies and potential biases.
Innovation Solution
Generate synthetic 3D medical images using diffusion models, such as Denoising Diffusion Probabilistic Models (DDPM) and ControlNet, to create image patches with organ contours, reducing the need for manually contoured data and enabling training across various imaging types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually contoured data is collected and processed for training segmentation models, then model accuracy can be improved, but the time and resources required increase significantly
Solution Approach 1:
The patent uses diffusion models to generate synthetic medical images that copy the statistical properties and anatomical structures of real medical images. These synthetic images serve as training data for segmentation models, replacing the need to manually collect and process large volumes of real annotated medical images, thereby reducing time and resource requirements while maintaining model training effectiveness
Solution Approach 2:
The patent pre-generates large datasets of synthetic medical images with embedded ground truth annotations before segmentation model training begins. This preliminary action creates a ready-to-use training dataset that eliminates the time-consuming processes of manual image collection, annotation, and validation that would otherwise be required
2Reliability
If a large volume of manually contoured data is gathered for training, then segmentation model performance improves, but the complexity and cost of data processing increase
Solution Approach 1:
Synthetic medical images generated by diffusion models replicate the essential characteristics of real medical images including anatomical structures, noise patterns, and intensity distributions. This copying approach provides sufficient training data without requiring complex data processing pipelines for collection, annotation, and quality control of real patient data
Solution Approach 2:
The diffusion model serves multiple functions: it generates synthetic images, creates ground truth annotations, and can be fine-tuned for different imaging modalities and anatomical regions. This multi-functionality reduces the need for separate data processing systems for different data types and purposes
3Adaptability or versatility
If manually annotated data is used for training across different imaging modalities, then model versatility improves, but the annotation effort and time required increase
Solution Approach 1:
The patent fine-tunes the diffusion model by changing training parameters to match different imaging modalities (CT, MRI, ultrasound) and anatomical regions. This allows the generation of modality-specific synthetic images with appropriate noise characteristics and intensity distributions, enabling versatile segmentation models without manual re-annotation for each modality
Solution Approach 2:
The diffusion model learns and copies the statistical properties and appearance characteristics of each imaging modality from relatively small amounts of real data, then generates large volumes of synthetic images for that modality. This copying approach achieves cross-modality versatility without requiring extensive manual annotation across all modalities
4Reliability
If real medical images are used for training segmentation models, then model generalization to real data improves, but the need for extensive manual annotation reduces efficiency
Solution Approach 1:
The diffusion model generates synthetic images that copy the realistic appearance, noise patterns, and anatomical variations of real medical images. These synthetic images serve as effective training data that improves model generalization to real data while avoiding the efficiency losses associated with manual annotation of real patient images
Solution Approach 2:
The diffusion model automatically generates images with embedded ground truth annotations without requiring manual intervention. This self-service capability produces unlimited training data with perfect annotations, improving both model generalization and development efficiency by eliminating the bottleneck of manual annotation
Data Source
AI summary
Systems, apparatus, instructions, and methods for model generation and deployment are disclosed. An example system includes: memory and processor circuitry to at least: train a first diffusion model using a first set of images; fine-tune the first diffusion model using a set of contours to form a second diffusion model; generate synthetic image patches using the second diffusion model and at least one contour; train a segmentation model using the synthetic image patches; and deploy the segmentation model to inference on a second set of images. Another example apparatus includes: a first diffusion model trained using a first set of images; a second diffusion model formed from the first diffusion model tuned using a set of contours, the second diffusion model to generate synthetic image patches using at least one contour; and a segmentation model trained using the synthetic image patches and deployed to inference on input images.