Hyperspectral Endoscopic Image Analysis for Esophageal Metastasis
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Solution Overview
Problem
Current methods for detecting esophageal cancer metastasis, such as CT and PET, are invasive and lack accuracy in early-stage detection, leading to late diagnoses and poor prognosis.
Innovation Solution
An image analysis method using convolutional neural networks (CNN) and hyperspectral imaging to analyze endoscopic images, employing PCA, feature extraction, and convolutional processes to determine metastasis by matching spectral features with reference spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT and PET methods are used for detecting esophageal cancer metastasis, then detection capability is improved, but invasiveness increases and early-stage detection accuracy decreases
Solution Approach 1:
The patent replaces invasive mechanical imaging methods (CT, PET) with non-invasive optical imaging (hyperspectral endoscopy). The system uses optical sensors to capture spectral signatures of tissue, substituting mechanical scanning and radioactive tracers with light-based detection that provides comparable metastasis detection accuracy without invasiveness
Solution Approach 2:
The patent transforms visual inspection parameters into spectral parameters by measuring reflectance across multiple wavelengths (400-1000nm). This parameter transformation enables early-stage metastasis detection through subtle spectral changes in tissue that are not visible to the human eye, improving detection accuracy while remaining non-invasive
2Measurement precision
If conventional endoscopic imaging is used, then ease of operation is maintained, but measurement precision for metastasis detection deteriorates
Solution Approach 1:
The patent performs preliminary spectral library construction and PCA model development before clinical use. Reference spectra from known metastatic and non-metastatic tissues are collected and processed in advance to create classification models, enabling rapid real-time detection during endoscopy without increasing operational complexity
Solution Approach 2:
The patent introduces PCA transformation and spectral matching algorithms as intermediaries between raw spectral data and metastasis classification. These computational intermediaries automatically process complex spectral patterns and compare them against reference libraries, providing accurate detection while maintaining simple endoscopic operation
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
Provides accurate staging of esophageal cancer metastasis through non-invasive endoscopic imaging, improving survival rates by enabling timely treatment decisions.
Implementation Method 1
converting the input images into hyperspectral images according to the hyperspectral image information, and obtaining spectral information of the hyperspectral images
Data Source
AI summary
A method for image analysis of predicted cell metastasis is provided. A host performs principle component analysis (PCA) for analysis and conversion of reference images into hyperspectral image information. Then an image capture unit sends input images to the host. The host converts the input images into hyperspectral images according to the hyperspectral image information and gets spectral information of the hyperspectral images. Next the host selects a plurality of wave bands corresponding to esophageal cancer cells and performs feature computation of the spectral information to generate corresponding features images. Then the host performs convolution of the feature images with kernels to get a convolution result. Later the host matches the convolution result with sample spectra of sample images to get a comparison result. Lastly the host determines whether metastasis of esophageal cancer cells occurs according to the comparison result.