Image-Based Cell Clustering Without Manual Gating
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Solution Overview
Problem
Traditional fluorescence activated cell sorting relies on fluorescent markers, which provide limited morphological information and manual gating, which is time-consuming and biased, while some applications require morphological information and are not suitable for fluorescent markers.
Innovation Solution
An image-based unsupervised multi-model cell clustering framework using a multi-layer neural network for feature extraction and clustering, which includes a model repository with various models for different applications, allowing clustering and sorting without ground truth and replacing manual gating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fluorescence activated cell sorting is used, then cells can be sorted based on fluorescent markers, but morphological information is limited and manual gating is time-consuming
Solution Approach 1:
The patent replaces manual gating operations with an automated unsupervised learning system that uses deep neural networks to perform image-based cell clustering. The system automatically extracts morphological features and performs clustering without human intervention, substituting the mechanical manual gating process with an automated computational system that provides both high precision morphological analysis and time efficiency
Solution Approach 2:
The system employs unsupervised learning where the algorithm autonomously learns cell cluster structures from raw image data without requiring pre-labeled ground truth or manual gating expertise. The model self-organizes to identify cell populations based solely on morphological features, making the system self-sufficient and eliminating dependency on manual gating operations
2Adaptability or versatility
If fluorescent markers are used for cell sorting, then sorting can be performed, but applications requiring morphological information or not suitable for fluorescent markers are limited
Solution Approach 1:
The patent creates a universal image-based sorting system that can handle diverse applications through a single platform. The unsupervised learning model processes various cell types and applications (including those not suitable for fluorescent markers) using the same morphological feature extraction and clustering pipeline, making the system adaptable to multiple scenarios without requiring application-specific modifications
Solution Approach 2:
The system extracts and utilizes morphological information directly from cell images by employing deep neural networks to identify and process shape, size, and structural features. This extraction of morphological characteristics enables sorting based on inherent cell properties rather than requiring fluorescent labeling, preserving and leveraging the natural morphological information of cells
3Reliability
If manual gating is used to establish sorting criteria, then sorting can be performed, but the process is time-consuming and may be biased
Solution Approach 1:
The patent replaces manual gating operations with an automated unsupervised learning system that uses deep neural networks to perform image-based cell clustering. The system automatically extracts morphological features and performs clustering without human intervention, substituting the mechanical manual gating process with an automated computational system that provides both high precision morphological analysis and time efficiency
Solution Approach 2:
The system implements iterative refinement where clustering results are evaluated and used to improve subsequent clustering operations. The unsupervised learning model continuously optimizes its feature extraction and clustering parameters based on the data it processes, providing objective and consistent sorting criteria without manual bias
Data Source
AI summary
A framework that includes one or more feature extractors (models) and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train one or more feature extractors, which are based on a neural network including several convolutional layers. Once trained, the feature extractors are used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractors after they have been trained.