Multispectral Cell Image Segmentation for Complex Morphology Classification
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Solution Overview
Problem
Current cytometric methods struggle to accurately classify complex cell morphologies such as sickle cells and cancer cells due to heterogeneous shapes, relying on manual image analysis and limited data from flow cytometry.
Innovation Solution
A computer-automated cell classification system using a multispectral imaging flow cytometer to acquire and spatially align brightfield, side scatter, and fluorescent images, employing a classifier engine for multispectral segmentation and iterative model fitting to detect complex morphologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional flow cytometry is used, then high-throughput cell analysis is achieved, but detailed morphological information is lost
Solution Approach 1:
The patent combines brightfield imaging and fluorescent imaging channels into a unified analysis system. The brightfield channel captures morphological information (cell size, shape, texture) while the fluorescent channel provides molecular markers, and both are processed together through machine learning algorithms to achieve comprehensive cell characterization without losing either morphological or molecular data
Solution Approach 2:
The patent transitions from traditional single-parameter flow cytometry to multi-dimensional image-based analysis. By capturing cells in 2D images with multiple channels (brightfield and fluorescent) and processing them through deep learning models, the system extracts features across multiple dimensions including spatial distribution, morphological characteristics, and molecular expression patterns simultaneously
2Loss of information
If microscopy is used, then detailed morphological information is obtained, but throughput is reduced
Solution Approach 1:
The patent implements automated preprocessing steps including image normalization, background subtraction, and cell segmentation before analysis. Machine learning models are pre-trained on large datasets to automatically identify and extract relevant features, eliminating the need for manual image processing and enabling high-throughput automated analysis with detailed morphological characterization
Solution Approach 2:
The patent replaces manual or semi-automated image analysis methods with machine learning-based automated analysis systems. Deep learning models automatically process images, extract features, and classify cells without human intervention, dramatically increasing throughput while maintaining detailed morphological analysis capabilities
3Loss of information
If separate analysis of brightfield and fluorescent channels is performed, then comprehensive data is collected, but analysis complexity increases
Solution Approach 1:
The patent merges the analysis of brightfield and fluorescent channels into a single integrated machine learning pipeline. Rather than analyzing channels separately and combining results, the system processes both channels simultaneously through a unified model that learns correlations between morphological features (from brightfield) and molecular markers (from fluorescent), simplifying the overall analysis workflow while capturing interactions between different data types
Data Source
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AI summary
A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types.