Deep Learning Cell Nucleus Segmentation for Consistent Tumor Diagnosis
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Solution Overview
Problem
The shortage of pathologists and the time-consuming training required for accurate histopathological diagnosis of tumors lead to delays in treatment and increased workload, with existing image analysis methods relying heavily on human feature extraction, which is prone to variability.
Innovation Solution
An image analysis method using a deep learning algorithm of a neural network to generate data indicating the region of a cell nucleus, utilizing training data from bright field and fluorescence images to improve accuracy and efficiency in distinguishing cell nuclei from other regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histopathological diagnosis by pathologists is used, then diagnosis accuracy is maintained, but the time required for training and the workload increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human pathologist observation and diagnosis with an automated image analysis system using deep learning algorithms. The system processes histopathological images through neural networks that have been trained on labeled datasets, automatically identifying cell nuclei regions and generating diagnostic information without requiring human pathologists to perform manual observation, thereby eliminating the need for extensive training time while maintaining diagnostic accuracy
2Ease of operation
If manual feature extraction by pathologists is used, then diagnosis can be performed, but the ability of the person greatly influences the performance and variability increases
Solution Approach 1:
The patent implements self-service through automated image analysis where the system performs feature extraction and diagnosis independently without human intervention. The deep learning algorithm automatically processes histopathological images, extracts relevant features, and generates diagnostic information, eliminating the variability introduced by different pathologists' manual feature extraction abilities while maintaining ease of operation
Solution Approach 2:
The patent transforms the diagnostic process by changing from manual parameter assessment to automated parameter extraction through deep learning. The system converts image data into quantitative features through neural network processing, standardizing the extraction process and ensuring consistent parameter measurement across different cases, thereby improving reliability while keeping the operation simple
3Productivity
If more pathologists are trained to handle the workload, then diagnosis capacity increases, but the training time and resource requirements increase
Solution Approach 1:
The patent replaces the need for training additional human pathologists with an automated image analysis system that can process multiple images simultaneously. The deep learning-based system increases diagnosis capacity by automating the analysis process, allowing parallel processing of numerous histopathological images without requiring proportional increases in trained personnel, thereby boosting productivity without the associated training time costs
Data Source
AI summary
The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.