Stroke Lesion Segmentation via CGAN Image Translation
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Solution Overview
Problem
Current methods for ischemic stroke lesion segmentation, such as CTP and DWI, face challenges in accurately identifying infarcted core regions due to lower signal-to-noise ratios and time-consuming manual processes, necessitating an automated and efficient image processing solution.
Innovation Solution
The implementation of a system using Conditional Generative Adversarial Networks (CGANs) that translates CT perfusion maps into MR-like images, combined with Fully Convolutional Neural Networks (FCNs) for semantic segmentation, to enhance the delineation of infarcted core tissue areas, leveraging deep learning techniques for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CTP is used for stroke lesion segmentation, then cost, speed, and availability are improved, but signal-to-noise ratio and identification accuracy of ischemic core deteriorate
Solution Approach 1:
The patent uses a trained neural network model to generate synthetic DWI images from CTP images. This copying approach allows the system to produce high-quality DWI-like images without actually performing a DWI scan, thus maintaining the speed and availability benefits of CTP while achieving the superior signal-to-noise ratio of DWI for identifying ischemic core regions
Solution Approach 2:
The neural network model acts as an intermediary that translates CTP images into DWI-like images. This intermediary transformation enables the system to leverage the advantages of both modalities: the accessibility of CTP and the diagnostic quality of DWI for lesion segmentation
2Measurement precision
If manual segmentation is performed by radiologist, then segmentation accuracy is improved, but time consumption increases
Solution Approach 1:
The system employs automated neural network models that perform segmentation independently without requiring radiologist intervention. The models process images and generate segmentation results automatically, eliminating manual labor while maintaining high accuracy through sophisticated deep learning algorithms trained on annotated data
Solution Approach 2:
The patent replaces the manual mechanical process of radiologist segmentation with an automated computational system. The neural networks automatically identify and segment lesions based on learned patterns from training data, substituting human expertise with algorithmic processing that is both fast and highly accurate
Data Source
AI summary
A system and method for performing image processing. The method includes receiving an image of a first modality and a real image of a second modality, the image of the first modality and the image of the second modality capturing respective images of a same subject, applying a first trained model to the image of the first modality to generate an artificial image mimicking the image of the second modality, applying a second trained model to the artificial image mimicking the image of the second modality and data of the image of the first modality, and outputting at least one conclusion regarding the generated artificial image.