Multi-Resolution Neural Networks for Medical Image Segmentation
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Solution Overview
Problem
Current image segmentation technologies face challenges in accurately and reliably segmenting objects within images, particularly in medical and military fields, due to difficulties in automated segmentation and the need for large labeled datasets, especially when dealing with different imaging modalities like CT and MRI.
Innovation Solution
The development of multi-modal, multi-resolution deep learning neural networks, specifically Generational Adversarial Networks (GANs) and Multi-Modality, Multi-Resolution Residual Networks (MMRRN), which synthesize realistic images and incorporate constraints for structure, shape, location, and appearance to enhance segmentation accuracy, and utilize deep supervision for feature selection and dynamic feature fusion across multiple modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation by specialized personnel is used, then segmentation accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system enables automated self-segmentation of medical images through deep learning neural networks, eliminating the need for manual intervention by specialized personnel while maintaining high segmentation accuracy through learned features from training data
Solution Approach 2:
The patent replaces the mechanical manual segmentation process with an automated computational system using convolutional neural networks and adversarial generative models, substituting human expertise with algorithmic processing to improve efficiency
2Productivity
If automated segmentation is implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary training on large datasets of labeled medical images before deployment, pre-learning anatomical structures and segmentation patterns to ensure high accuracy when performing automated segmentation on new images
Solution Approach 2:
The patent introduces adversarial generative networks as an intermediary component that synthesizes realistic medical images with accurate annotations, serving as a bridge to improve the training data quality and thereby enhance the precision of the segmentation model
3Measurement precision
If large labeled datasets are used for training, then measurement precision is improved, but loss of substance worsens
Solution Approach 1:
The adversarial generative network acts as an intermediary that synthesizes additional training data, allowing the system to achieve high model accuracy without requiring proportionally large amounts of real labeled medical images
Solution Approach 2:
The system creates synthetic copies of medical images through generative adversarial networks, producing realistic training data that mimics the characteristics of real patient data while preserving patient privacy and reducing the need for extensive real labeled datasets
Data Source
AI summary
Systems and methods for multi-modal, multi-resolution deep learning neural networks for segmentation, outcomes prediction and longitudinal response monitoring to immunotherapy and radiotherapy are detailed herein. A structure-specific Generational Adversarial Network (SSGAN) is used to synthesize realistic and structure-preserving images not produced using state-of-the art GANs and simultaneously incorporate constraints to produce synthetic images. A deeply supervised, Multi-modality, Multi-Resolution Residual Networks (DeepMMRRN) for tumor and organs-at-risk (OAR) segmentation may be used for tumor and OAR segmentation. The DeepMMRRN may combine multiple modalities for tumor and OAR segmentation. Accurate segmentation may be realized by maximizing network capacity by simultaneously using features at multiple scales and resolutions and feature selection through deep supervision. DeepMMRRN Radiomics may be used for predicting and longitudinal monitoring response to immunotherapy. Auto-segmentations may be combined with radiomics analysis for predicting response prior to treatment initiation. Quantification of entire tumor burden may be used for automatic response assessment.