Cell Tracking via Nucleus Dictionary and State Space Model
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Solution Overview
Problem
Existing automatic cell tracking methods fail to accurately and automatically measure locomotion speeds of human epidermal keratinocytes, especially in high cell density conditions, and require human intervention, limiting their precision and applicability in evaluating stem cell quality for regeneration medicine.
Innovation Solution
A method using a dictionary of cell nuclei images and a state space model for tracking cells, where the position of the most adjacent cell within a predetermined distance is used as observation data to detect and track cell positions, enabling automatic calculation of locomotion speeds and identifying high proliferative activity cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to measure cell locomotion speed, then measurement precision can be maintained, but productivity is reduced due to heavy human burden and time consumption
Solution Approach 1:
The system enables automatic self-evaluation of cell locomotion speed through image analysis and tracking algorithms, eliminating the need for manual measurement while maintaining precision. The computer automatically calculates locomotion speed based on position changes between time-lapse images.
Solution Approach 2:
Manual mechanical measurement methods are replaced with automated image processing and computer-based tracking systems. The patent uses digital image analysis to detect cell positions and calculate locomotion speed, substituting human operation with automated computational methods.
2Productivity
If existing automatic tracking methods are used, then productivity is improved, but measurement precision deteriorates due to failure to accurately track cells in high density conditions
Solution Approach 1:
The system performs preliminary detection of cell positions in each time-lapse image before tracking, using image processing to identify and record precise cell locations. This preliminary action enables accurate subsequent tracking even in high density conditions where cells are closely packed.
Solution Approach 2:
The tracking system uses feedback from continuous image analysis to adjust and maintain accurate cell position detection. By comparing positions across multiple time points and using tracking algorithms, the system compensates for challenges in high density conditions and maintains measurement precision.
3Measurement precision
If invasive methods are used to identify stem cells, then identification accuracy is improved, but the cells may be damaged or their natural behavior altered
Solution Approach 1:
Invasive mechanical or chemical identification methods are replaced with non-invasive optical imaging and computer-based analysis. The patent uses time-lapse image capture and automated tracking to identify stem cells based on their locomotion characteristics, eliminating the need for physical intervention that could damage cells or alter their behavior.
Data Source
AI summary
Even for the case where cells such as human epidermal keratinocytes form a dense colony, or the case where cell contours are indefinite, each of the cells is automatically tracked with high precision, and behavior of each cell is analyzed with good precision. There is provided a method for analyzing behavior of a cell, which comprises a detection step of detecting positions of a plurality of cells for every frame of time-lapse images, while determining whether a candidate region extracted from the frame is a cell region by using a dictionary containing image data of cell nuclei; and a tracking step of tracking each cell by using a state space model using position of a most adjacent cell within a predetermined distance from a predicted position as observation data. When any cell is not found within a certain distance from the predicted position, data are considered missing.