Virtual IHC Neural Network for Rapid Nuclei-Level Cancer Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing immunohistochemistry (IHC) techniques for diagnosing conditions like cancer are time-consuming, costly, and require specialized equipment and expertise, while machine learning methods, such as CNNs, are labor-intensive and prone to human bias, especially in challenging cases with overlapping nuclei and variable chromatin density.
Innovation Solution
A system and method using a convolutional neural network (CNN) trained on both H&E and IHC-stained tissue images, allowing for automated analysis of tissue samples without specialized equipment, by aligning and annotating H&E images with IHC data to create a virtual IHC (vIHC) for accurate cell classification and quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional immunohistochemistry (IHC) techniques are used for diagnosis, then diagnostic accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent creates a virtual copy of the IHC staining process using a convolutional neural network that processes H&E stained images. The CNN is trained on paired H&E and IHC images to generate a virtual IHC image that replicates the diagnostic information of actual IHC staining without requiring the physical staining process, thereby reducing time and cost while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical and chemical IHC staining process with a computational system. Instead of using antibodies and chemical reactions to detect antigens, the system uses a trained CNN to predict IHC staining patterns from H&E images, substituting a biochemical process with an artificial intelligence-based computational process
2Measurement precision
If traditional immunohistochemistry (IHC) techniques are used for diagnosis, then diagnostic accuracy is improved, but device complexity and expertise requirements increase
Solution Approach 1:
The patent makes the diagnostic system universal by training the CNN on diverse tissue types and conditions. The single trained model can process various H&E stained images across different tissue types and diagnostic scenarios, eliminating the need for multiple specialized IHC staining protocols and reducing dependence on specialized equipment and expert knowledge
Solution Approach 2:
The patent replaces specialized IHC equipment and laboratory infrastructure with a computational system that runs on standard computing hardware. The diagnostic capability is transferred from a complex wet-lab environment requiring specialized equipment to a software-based system that can operate on conventional computers or servers
3Extent of automation
If machine learning methods are used for cell analysis, then automation is improved, but human bias and labor intensity increase
Solution Approach 1:
The patent implements self-service by training the CNN to learn diagnostic patterns directly from training data without requiring manual annotation or human intervention during the diagnostic process. The system automatically processes H&E images and generates virtual IHC results, eliminating human bias and reducing labor intensity while maintaining high automation levels
Data Source
AI summary
This provides a system and method for analyzing and diagnosing conditions, such as cancer, based upon stained tissue sample slides prepared by users from patient tissue taken, for example, in a biopsy procedure. A convolutional neural network (CNN) is generated at training time. Slide images are acquired/scanned, and individual cell nuclei from the images are annotated, using IHC. H&E images are acquired/scanned prior to a washout intermediate step from the same slides/tissue samples. IHC is performed on the same tissue layer, which was scanned and used to retrospectively annotate the H&E WSI. IHC is used to annotate a ground truth mask for machine learning. The resultant process achieves a high degree of spatial resolution in detecting individual IHC positive nuclei, yielding process that is almost entirely automated, employing materials that are commonly available in clinical practice. In runtime, users access the CNN to perform analysis on patient slides.