Multi-Angle Cell Imaging and Deep Learning Sorting
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Solution Overview
Problem
Current methods for cell analysis, particularly in cytology smears, are time-consuming, subjective, and prone to errors due to the difficulty in detecting rare cells and distinguishing features obscured by contaminant cells and angle issues during microscopic imaging.
Innovation Solution
A method involving transporting cells through a flow channel, capturing images from multiple angles, and analyzing them using a deep learning algorithm to sort and classify cells, which includes rotating the cells and applying a velocity gradient to enhance image capture and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual microscopic imaging and analysis is used to identify cell type and diagnose disease, then diagnostic capabilities are provided, but the process is time-consuming, subjective, and prone to error
Solution Approach 1:
The patent replaces manual mechanical microscopic analysis with an automated digital imaging and deep learning analysis system. Multiple cameras capture images from different angles, and deep learning algorithms automatically analyze cell features, eliminating subjective human interpretation and significantly reducing analysis time while improving diagnostic accuracy and consistency
Solution Approach 2:
The system creates multiple digital copies of cell images from different viewing angles using multiple cameras. These digital copies are then processed by deep learning algorithms to extract comprehensive cell features, allowing thorough analysis without the time constraints of manual single-angle microscopic examination
2Quantity of substance
If contaminant cells are present in the sample, then cell population is increased, but detection of rare cells and specific disease features becomes difficult
Solution Approach 1:
The patent adds the dimension of multiple viewing angles by using cameras positioned at different locations to capture images of cells from various perspectives. This multi-angle imaging approach allows the deep learning system to distinguish rare cells and disease features from contaminant cells by analyzing their three-dimensional morphological characteristics, significantly improving detection capability in complex samples
Solution Approach 2:
The deep learning algorithm serves as an intermediary that processes multi-angle image data to identify and distinguish rare cells from contaminant cells. The algorithm extracts meaningful features from the complex multi-angle imagery, enabling reliable detection of rare disease-related cells even in the presence of numerous contaminant cells
3Device complexity
If cells are imaged from a single angle during microscopic analysis, then imaging simplicity is maintained, but essential cell information may be obscured
Solution Approach 1:
The patent transitions from single-angle two-dimensional imaging to multi-angle three-dimensional imaging by positioning cameras at different locations around the flow channel. This captures comprehensive cell morphological information from multiple perspectives, ensuring that essential cell features are not obscured while maintaining manageable system complexity through standardized camera arrangements
Solution Approach 2:
The multi-camera imaging system serves multiple functions simultaneously: capturing cell images from different angles, providing multi-perspective views for deep learning analysis, and enabling comprehensive cell characterization. This universal imaging approach maximizes information extraction from each cell while the automated processing keeps overall system complexity manageable
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate cell sorting and classification, reducing human error and improving throughput by utilizing deep learning for precise identification of cell features from varied angles, thereby enhancing diagnostic capabilities.
Implementation Method 1
transporting a cell through a flow channel
Implementation Method 2
applying a velocity gradient across the cell to rotate the cell
Implementation Method 3
capturing a plurality of images of the cell from a plurality of different angles
Data Source
AI summary
The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.