Optical Attenuation Coefficient Segmentation for Macular Degeneration
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Solution Overview
Problem
Current methods for detecting and quantifying geographic atrophy in age-related macular degeneration lack accuracy and efficiency, particularly in predicting the progression of the disease, which hinders clinical practice and clinical trial effectiveness.
Innovation Solution
A computer-implemented method using optical coherence tomography (OCT) data to calculate optical attenuation coefficients, enabling the identification and segmentation of geographic atrophy areas, and predicting enlargement rates through machine learning models and attribute measurements in adjacent areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image analysis is implemented to detect and segment geographic atrophy, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The patent segments the geographic atrophy detection process into distinct functional modules: OCT data acquisition, optical attenuation coefficient calculation, geographic atrophy segmentation, adjacent area attribute measurement, and enlargement rate prediction. Each module performs a specific task, improving overall system productivity while managing complexity through functional decomposition
Solution Approach 2:
The patent introduces optical attenuation coefficient maps as an intermediary representation between raw OCT data and geographic atrophy segmentation. This intermediate data structure simplifies the segmentation process by providing a quantitative measure of light attenuation that directly correlates with tissue atrophy, thereby improving detection efficiency without proportionally increasing system complexity
2Measurement precision
If optical attenuation coefficient calculation is performed for each pixel to improve segmentation accuracy, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent transforms the OCT signal intensity data into optical attenuation coefficient values through a mathematical transformation. This parameter change converts the raw imaging data into a physically meaningful quantity that directly reflects tissue optical properties, improving segmentation accuracy while the calculation can be performed efficiently using established algorithms
Solution Approach 2:
The patent creates optical attenuation coefficient maps as a computational copy of the OCT data with enhanced diagnostic information. Rather than modifying the original OCT acquisition process, the attenuation coefficients are calculated from existing OCT signals, providing improved measurement precision without requiring additional hardware or excessive energy consumption
3Measurement precision
If machine learning models are used to predict enlargement rates based on adjacent area attributes, then prediction accuracy is improved, but device complexity and training time increase
Solution Approach 1:
The patent performs preliminary measurement of adjacent area attributes (such as retinal layer thickness, reflectivity, and structural characteristics) before applying the machine learning prediction model. This preliminary data collection and feature extraction prepares the input data in advance, improving prediction accuracy while allowing the model to focus on processing pre-computed features rather than raw data
Solution Approach 2:
The patent applies different analysis approaches to different regions: the geographic atrophy area is segmented using optical attenuation coefficients, while the adjacent area uses attribute measurements of specific retinal structures. The machine learning model then integrates these locally optimized measurements to predict enlargement rates, improving overall prediction accuracy without requiring a single complex model for all regions
Data Source
AI summary
In some embodiments, a computer-implemented method of automatically predicting progression of age-related macular degeneration is provided. An image analysis computing system receives optical coherence tomography data (OCT data). The image analysis computing system determines an optical attenuation coefficient for each pixel of the OCT data to create optical attenuation coefficient data (OAC data) corresponding to the OCT data. The image analysis computing system determines an area exhibiting geographic atrophy based on at least one of the OCT data and the OAC data. The image analysis computing system measures one or more attributes within an adjacent area that is adjacent to the area exhibiting geographic atrophy, and the image analysis computing system determines a predicted enlargement rate based on the one or more attributes within the adjacent area.