Cytology Slide ROI Detection for Faster Cell Classification
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Solution Overview
Problem
Current bone marrow cytology analysis methods are tedious, prone to inter-observer variation, and lack efficient automated solutions for region-of-interest detection, object detection, and object classification, leading to delayed or incorrect diagnoses in hematological disorders.
Innovation Solution
An automated system using two neural networks to identify relevant image portions and detect features within cytology specimens, including region-of-interest detection, object detection, and classification, utilizing a first neural network to select regions of interest and a second neural network for feature identification, with a statistical model to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual cell counting is used for cytology analysis, then diagnostic accuracy can be maintained, but processing time increases and inter-observer variation occurs
Solution Approach 1:
The whole slide image is divided into multiple image portions (tiles) that are processed independently by the neural network. This segmentation allows the system to handle large images efficiently while maintaining the ability to detect all relevant features across the entire specimen, resolving the contradiction between processing speed and diagnostic accuracy.
Solution Approach 2:
The patent replaces manual mechanical cell counting by pathologists with an automated neural network system. The neural network automatically detects, counts, and classifies cells, eliminating inter-observer variation and significantly reducing processing time while maintaining diagnostic accuracy through learned patterns from training data.
2Measurement precision
If the entire cytology specimen is analyzed in detail, then diagnostic accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the whole slide image into multiple smaller image portions and processes them in parallel. This allows detailed analysis of all regions while reducing the computational burden on any single processing unit and enabling faster overall processing through parallelization.
Solution Approach 2:
The neural network is pre-trained on extensive datasets of cytology specimens, learning to identify relevant features and regions of interest. This preliminary training enables the system to quickly and accurately process new specimens without requiring time-consuming manual analysis, achieving both high precision and fast processing.
3Reliability
If multiple image portions are processed to ensure comprehensive analysis, then feature detection completeness improves, but computational complexity increases
Solution Approach 1:
By dividing the large whole slide image into smaller manageable image portions, the system reduces the computational complexity of processing each individual portion while maintaining comprehensive coverage of the entire specimen. This segmentation strategy enables parallel processing and reduces memory requirements.
Solution Approach 2:
The neural network is designed to perform multiple functions simultaneously - detecting cells, classifying cell types, identifying regions of interest, and quantifying features - all within a single unified model. This multi-functionality reduces overall system complexity compared to using separate specialized systems for each task.
Data Source
AI summary
Computer-implemented methods and systems are provided for automatically identifying features of a cytology specimen. An example method can involve dividing a whole slide image of the cytology specimen into a plurality of image portions; applying a first neural network to each image portion to identify one or more relevant image portions; and applying a second neural network to a first relevant image portion and a second relevant image portion to generate a respective first and second cell data. Each relevant image portion can be an image portion containing a region of interest for the cytology specimen. The method can further involve comparing the first and second cell data to determine whether a similarity threshold is satisfied; and in response to determining the similarity threshold is not satisfied, continuing to apply the second neural network to a subsequent relevant image portion until the similarity threshold is satisfied.