Machine Learning T-Cell Classification From Optical Images
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Solution Overview
Problem
Existing methods for characterizing cells, particularly T cells, during ex vivo manufacturing or prior to administration, are time-consuming and risk contamination, often relying on direct manipulation with reagents that can interfere with cell quality or function.
Innovation Solution
Utilizing machine learning methods, specifically convolutional neural networks, support vector machines, and random forests, to classify T cells based on morphological, optical, intensity, and phase features extracted from image data, without physical manipulation, thereby improving classification efficiency and reducing contamination risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct manipulation with reagents (magnetic beads, immunoaffinity-based reagents) is used to characterize cells, then cell classification can be achieved, but cell quality and function are interfered with and contamination risk increases
Solution Approach 1:
The patent replaces mechanical/chemical manipulation systems (magnetic beads, immunoaffinity reagents) with an optical analysis system. Image data is captured and processed through machine learning models to classify cells based on morphological, optical, intensity, and phase features, eliminating the need for physical reagent interaction with cells.
Solution Approach 2:
The patent creates optical copies (images) of cells for analysis instead of manipulating the actual cells. Multiple image modalities (brightfield, phase contrast, fluorescence) are captured and processed to extract features for classification, allowing analysis without direct reagent contact with the biological samples.
2Measurement precision
If existing reagent-based methods are used for cell characterization, then cell classification is achieved, but the process becomes time-consuming
Solution Approach 1:
The patent implements continuous image capture and processing, where cells are imaged and classified in real-time or near-real-time as they flow through the system. The machine learning model processes image data continuously, eliminating the discrete, sequential steps required by reagent-based methods.
Solution Approach 2:
The patent performs preliminary feature extraction from multiple image modalities before classification. By pre-processing images to extract morphological, optical, intensity, and phase features, the system prepares data for rapid classification without requiring time-consuming post-processing or additional reagent steps.
3Measurement precision
If machine learning methods with multiple image features are used, then classification accuracy reaches up to 100%, but system complexity increases
Solution Approach 1:
The patent uses a single imaging system that captures multiple image modalities (brightfield, phase contrast, fluorescence) simultaneously or sequentially. The same optical system serves multiple functions by extracting different feature types (morphological, optical, intensity, phase) from the captured images, reducing the need for separate specialized devices.
Solution Approach 2:
The patent combines multiple image processing and classification functions into a unified machine learning model. The model integrates features from different image modalities and feature types (morphological, optical, intensity, phase) in a single classification process, simplifying the overall system architecture despite the complexity of individual components.
Data Source
AI summary
Provided are methods for classifying cells, such as T cells, using machine learning methods. The methods can be used to classify different subsets or types of cells in a mixed population of cells.