Single-Cell Trajectory Tracking in Microfluidic Devices
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Solution Overview
Problem
Existing automated computational tools are unable to accurately track single-cell trajectories in microfluidic channels due to cell collisions, detachment, varying speeds, and low contrast between foreground and background, which complicates the extraction of cell trajectories from video recordings.
Innovation Solution
A system and method that utilize processors, cameras, and microfluidic devices to record and analyze cell events within frames, distinguishing cells from background illumination, determining cell types, and calculating trajectories based on center coordinates, pixel numbers, and radii, while segmenting or merging events as necessary to account for cell collisions and changes in cell state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing automated computational tools are used for cell tracking, then the tracking process is automated, but the accuracy of trajectory extraction deteriorates due to cell collisions, detachment, varying speeds, and low contrast
Solution Approach 1:
The patent segments the cell tracking problem into multiple sub-tasks: background subtraction to separate cells from low-contrast backgrounds, event detection to identify cell positions in each frame, trajectory association to link events across frames, and collision handling to manage cell interactions. This segmentation allows each sub-task to be optimized independently, maintaining automation while improving overall tracking accuracy
Solution Approach 2:
The patent introduces intermediate data structures and processing steps as mediators: background models serve as intermediaries to enhance cell visibility, event representations serve as intermediaries to standardize cell detections, and trajectory hypotheses serve as intermediaries to guide association. These intermediaries bridge the gap between raw video data and accurate trajectories, enabling automated tracking to overcome low contrast and varying speeds
2Device complexity
If conventional trajectory extraction methods are used, then the process is simple, but the ability to handle cell collisions and detachment deteriorates
Solution Approach 1:
The patent implements dynamic adaptation in the tracking process: the background model is continuously updated to adapt to changing illumination conditions, the event detection thresholds are dynamically adjusted based on local contrast, and the trajectory association logic dynamically handles collisions by allowing temporary overlaps and predicting separation. This dynamic approach maintains reliability during cell collisions and detachment while keeping the overall process manageable
Solution Approach 2:
The patent incorporates feedback mechanisms where tracking results are continuously evaluated and used to improve subsequent tracking: detected cell positions feedback to update background models, trajectory prediction errors feedback to adjust association parameters, and collision detection feedback to trigger special handling routines. This feedback loop enables the system to reliably handle collisions and detachment by learning from past errors
3Speed
If high-speed recording is used to capture fast-moving cells, then the cell speed variation is captured, but the contrast between foreground and background deteriorates
Solution Approach 1:
The patent performs preliminary background subtraction and illumination normalization before trajectory extraction. By removing the background illumination pattern in advance and normalizing the image contrast, the system prepares enhanced images that maintain visibility of fast-moving cells even when recorded at high speeds where motion blur and exposure issues reduce contrast
Data Source
AI summary
A system for tracking single-cell movement trajectories is disclosed. The system can record, to a plurality of frames, cells (events) within a microfluidic device. Also, the system can identify an event within each frame including whether the event is a single cell or multiple cells. When the event appears differently between frames (e.g., single cell in one frame and multiple cells in another frame), the system can either segment or merge the cell(s). Then, the system can determine a trajectory for the events based on a position of the event in the frames. Further, the system can determine cell properties based on the trajectory of the events.