Retinal Lesion Analysis via OCT Coordinate Mapping
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Solution Overview
Problem
Current methods for analyzing retinal lesions in mouse models using optical coherence tomography (OCT) face challenges in accurately locating and distributing lesions due to the small size of mouse eyeballs and the lack of a macula, which complicates fundus photography and the integration of cross-sectional and quadrant distribution information.
Innovation Solution
A method that involves scanning the mouse posterior polar retina with OCT, acquiring lesion images, constructing a coordinate map of lesion distribution, calculating lesion quadrants, and counting the number of lesions using linear distance and angle measurements from the optic papilla center to the retinal layers, integrating cross-sectional and quadrant information for comprehensive lesion analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If OCT scanning is used to examine mouse retina, then high-resolution cross-sectional imaging is obtained, but accurate localization of retinal lesions becomes difficult due to small eyeball size and lack of fixed anatomical landmarks
Solution Approach 1:
The patent transforms the two-dimensional OCT cross-sectional images into a three-dimensional coordinate system by integrating fundus photography information. This dimensional transformation allows precise localization of retinal lesions by mapping their positions in x, y, and z coordinates, solving the problem of accurate lesion localization in small mouse eyeballs without requiring complex additional imaging equipment.
Solution Approach 2:
The patent combines OCT imaging technology with fundus photography to create an integrated analysis system. By merging the cross-sectional structural information from OCT with the surface mapping information from fundus photography, the system achieves accurate lesion localization while maintaining operational simplicity and avoiding the need for separate complex imaging systems.
2Extent of automation
If deep learning algorithms are used for lesion classification and extraction, then automated analysis is achieved, but the cost and operational complexity increase significantly
Solution Approach 1:
The patent replaces expensive and complex deep learning algorithms with simpler, more accessible image processing methods that can be implemented in ordinary medical units and animal laboratories. This substitution maintains the essential automation of lesion analysis while dramatically reducing the complexity requirements of the experimental platform, making the technology accessible to broader user bases.
Solution Approach 2:
The patent employs automated image processing algorithms that can independently perform lesion detection, classification, and measurement without requiring sophisticated artificial intelligence experimental platforms. The system processes images through standardized computational steps that are self-contained and do not depend on external complex infrastructure, thereby achieving automation with minimal platform complexity.
3Loss of information
If cross-sectional OCT information alone is used, then retinal layer invasion depth is determined, but overall location characteristics and quadrant distribution of lesions cannot be reflected
Solution Approach 1:
The patent merges cross-sectional OCT information with en face fundus photography information to create a comprehensive lesion analysis system. This integration preserves all the layer invasion depth information from OCT while adding the quadrant distribution and overall location characteristics from fundus photography, achieving complete lesion characterization without requiring separate complex analysis systems.
Solution Approach 2:
The patent supplements the two-dimensional cross-sectional OCT data with three-dimensional spatial coordinates by integrating fundus photography. This dimensional enhancement allows the system to determine not only the depth of lesion invasion into retinal layers but also the precise location and quadrant distribution of lesions, providing comprehensive spatial information through coordinate mapping.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a simple and intuitive analysis of retinal lesion distribution, reducing the complexity of lesion localization and enabling accurate statistical analysis of lesion positions and numbers across different quadrants and retinal layers, suitable for animal model research.
Implementation Method 1
scanning a mouse posterior polar retina based on optical coherence tomography (OCT), and acquiring lesion images of the mouse posterior polar retina
Data Source
AI summary
Disclosed is a method for analyzing a distribution of retinal lesions in a mouse model, including: scanning a mouse posterior polar fundus based on optical coherence tomography (OCT), and acquiring lesion images of the mouse posterior polar fundus; acquiring lesion distribution coordinates based on the lesion images of the mouse posterior polar fundus; constructing a coordinate map of a lesion distribution rule based on the lesion distribution coordinates; and acquiring lesion distribution in quadrants based on the coordinate map of the lesion distribution rule, and calculating and counting a number of lesions in each quadrant.