Superpixel Feature Vector Morphology Identification
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Solution Overview
Problem
Current methods for identifying morphology and pathologies on histology slides are inefficient and prone to human error, with automation often resulting in false positives and insufficient predictive power, and lack effective automation for selecting appropriate analysis models for diverse clinical contexts.
Innovation Solution
A method that identifies and names superpixels in tissue samples using feature vectors, allowing for automatic suggestion and confirmation of morphologies, with a learning system that locks names after multiple confirmations, and suggests regions of interest based on medical specialty and clinical context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel classification is performed to identify morphology, then measurement precision is improved, but processing time increases significantly requiring hours per slide
Solution Approach 1:
The patent segments the image into superpixels (groups of pixels) rather than processing individual pixels. This segmentation reduces the number of units to classify from millions of pixels to a manageable number of superpixels, maintaining morphology identification accuracy while dramatically reducing processing time from hours to minutes per slide.
Solution Approach 2:
The patent applies classification to superpixels (excessive action at the pixel level) rather than requiring complete pixel-by-pixel analysis. By classifying larger superpixel regions, the system achieves sufficient accuracy without the time cost of exhaustive pixel-level processing, effectively applying partial action to the most critical regions.
2Productivity
If automated region selection is implemented to improve efficiency, then productivity is improved, but false positives increase and reliability decreases
Solution Approach 1:
The patent incorporates feedback mechanisms where pathologists review and confirm automated superpixel classifications. This feedback loop allows the system to learn from corrections and improve its accuracy over time, maintaining high reliability while achieving automated processing speeds. The feedback also enables the system to adapt to different tissue types and pathology patterns.
Solution Approach 2:
The system performs preliminary actions by pre-processing images to identify and segment potential regions of interest before final classification. This preliminary segmentation into superpixels creates a structured framework that guides subsequent classification, reducing false positives while maintaining high processing throughput.
3Measurement precision
If manual region selection is required before automated analysis, then measurement precision is improved, but device complexity increases and ease of operation decreases
Solution Approach 1:
The patent implements self-service functionality where the system automatically segments and classifies tissue images without requiring manual region selection. The automated superpixel-based system performs the analysis independently, eliminating the need for users to manually define regions while maintaining high accuracy through the intelligent segmentation and classification algorithms.
4Ease of operation
If predetermined thresholds are used for image analysis to simplify processing, then ease of operation is improved, but measurement precision deteriorates due to inability to adapt to varying clinical contexts
Solution Approach 1:
The patent transitions from static predetermined thresholds to dynamic classification criteria that adapt to different tissue types, pathology patterns, and clinical contexts. The superpixel-based system can adjust its analysis parameters and classification thresholds based on the specific image characteristics and diagnostic requirements, maintaining simplicity of operation while significantly improving measurement precision across diverse applications.
Data Source
AI summary
Locating morphology in a tissue sample is achieved with devices and methods involving storage of a plurality of feature vectors, each associated with a specific named superpixel of a larger image of a tissue sample from a mammalian body. A microscope outputs, in some embodiments, a live image of an additional tissue sample or a digitized version of the output is used. At least one superpixel of the image is converted into a feature vector and a nearest match between the first feature vector and the plurality of stored feature vectors is made. A first name suggestion is then made based on the nearest match comparison to a store feature vector. Further, regions of interest within the image can be brought to a viewer's attention based on their past history of selection, or that of others.