CNN Tissue Image Analysis with Dilated Convolutions
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Solution Overview
Problem
Current methods for processing images of tissue in histopathology are time-consuming and prone to variability in accuracy, relying heavily on human expertise and taking days to analyze large images.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) with dilated convolutions for image processing, which automatically segments images of tissue, reducing analysis time and enhancing accuracy by learning local and non-local features without increasing computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by histopathologists is used, then diagnostic accuracy can be maintained through expert experience, but analysis time increases to days and accuracy varies between experts
Solution Approach 1:
The patent replaces the manual mechanical analysis process performed by histopathologists with an automated computer-based image analysis system. The system uses digital image processing algorithms to automatically detect, segment, and characterize tissue structures, replacing the manual microscope examination and note-taking process. This substitution dramatically reduces analysis time from days to minutes while maintaining consistent diagnostic accuracy.
Solution Approach 2:
The image analysis system performs self-service by automatically processing histopathology images without requiring continuous human intervention. The system autonomously executes the complete analysis workflow including image acquisition, preprocessing, feature extraction, and diagnostic interpretation, enabling high-throughput processing of multiple slides efficiently.
2Productivity
If automated image analysis is implemented, then analysis speed increases, but accuracy may decrease without expert human judgment
Solution Approach 1:
The system employs multiple parameter changes to enhance automated analysis accuracy. It transforms images from RGB to HSV color space to better capture tissue characteristics, applies various preprocessing parameters (contrast enhancement, noise filtering), and adjusts segmentation thresholds dynamically. These parameter transformations enable the automated system to achieve diagnostic accuracy comparable to expert histopathologists.
Solution Approach 2:
The patent introduces intermediate processing steps as mediators between raw image data and final diagnosis. These include color normalization layers, artifact detection modules, and multi-scale feature extraction that bridge the gap between automated processing and expert-level interpretation, ensuring high diagnostic accuracy is maintained.
3Loss of information
If whole slide images are analyzed in full resolution, then diagnostic information is complete, but processing time increases significantly
Solution Approach 1:
The patent applies multi-scale segmentation to divide the analysis process into different resolution levels. It first performs coarse analysis at low resolution to identify regions of interest, then focuses detailed analysis only on those specific regions at high resolution. This hierarchical segmentation approach maintains complete diagnostic information while dramatically reducing overall processing time by avoiding full-resolution analysis of entire slides.
Solution Approach 2:
The system performs preliminary low-resolution scanning and preprocessing before detailed high-resolution analysis. It pre-identifies suspicious regions, pre-normalizes colors, and pre-detects artifacts at reduced resolution, preparing the data in advance so that subsequent high-resolution analysis can be focused and efficient, maintaining information completeness while reducing total processing time.
Data Source
AI summary
A computer implemented method of processing an image of tissue, comprising:inputting image data comprising a plurality of pixels into a first trained model, the first trained model generating a value corresponding to each of a plurality of pixels representing a feature relevant to disease diagnosis;wherein the first trained model comprises a convolutional neural network comprising a layer in which a first filter and a second filter are applied, at least one of the first filter and the second filter comprising a dilated convolution, wherein the output of the first filter and the second filter are combined and input into the subsequent layer.