CNN Cell Detection in Lens-Free Microscopy
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Solution Overview
Problem
Current methods for analyzing lens-free microscopy (LFM) data lack effective tools for detecting and tracing biological cells and reconstructing their spatio-temporal lineage, especially in the context of continuous monitoring of cell cultures, which is crucial for understanding cancer progression and drug development.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) for cell detection and a probabilistic model for lineage tracing, which generates probability maps from LFM images and reconstructs cell lineage forests by clustering and tracking multiple detections, employing residual learning and moral lineage tracing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If lens-free microscopy is used for continuous monitoring of cell cultures, then the cost and size of the system are reduced, but the complexity of analyzing the acquired images increases
Solution Approach 1:
The patent replaces traditional mechanical/optical image analysis methods with a computational approach using convolutional neural networks. The CNN automatically learns features and performs cell detection, counting, and tracking from the raw LFM images, substituting complex manual or algorithmic image processing with a trained deep learning model that handles the analysis complexity internally.
Solution Approach 2:
The patent uses fluorescence microscopy images as annotated training data to teach the CNN what cells look like. By creating labeled copies of ground truth cell positions and characteristics from fluorescence images, the system enables the CNN to learn accurate cell detection and tracking patterns, transferring knowledge from easily annotatable fluorescence data to the more challenging LFM data analysis.
2Device complexity
If traditional image analysis methods are used for cell detection in LFM images, then the implementation is simpler, but the accuracy of cell detection and lineage tracing decreases
Solution Approach 1:
The patent replaces traditional image processing algorithms with a convolutional neural network that has learned optimal detection features from training data. The CNN automatically identifies cells, determines their positions, and tracks them through time-lapse sequences, achieving superior accuracy compared to conventional methods while handling the complexity internally through the trained model architecture.
Solution Approach 2:
The CNN performs self-service by automatically detecting cells, counting them, and tracking their lineages without requiring manual intervention or complex post-processing. The model learns from annotated training data and then independently applies this knowledge to analyze new LFM images, performing the full analysis pipeline autonomously with high accuracy.
3Loss of information
If cell lineage tracing is performed in dense cell cultures, then more biological information is obtained, but the difficulty of detecting and measuring individual cells increases
Solution Approach 1:
The patent employs a feedback mechanism where the CNN process sequentially analyzes each frame of the time-lapse sequence and uses the detected cell positions and trajectories from previous frames to inform detection in subsequent frames. This temporal feedback allows the system to maintain accurate cell identification and lineage tracking even when cells are densely packed and overlapping in individual frames.
Solution Approach 2:
The system performs preliminary action by pre-training the CNN on annotated fluorescence microscopy images before deploying it to LFM data. This preliminary training establishes robust cell detection capabilities that can handle dense cultures. Additionally, the system performs preliminary cell identification in early frames to establish baseline positions and trajectories, making subsequent tracking in dense regions more accurate.
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
Enables reliable and automatic analysis of cell growth and migration dynamics, providing insights into cell cycle timings and population dynamics, with high accuracy in detecting and tracing cells, even in dense cultures, and effectively handling overlapping interference patterns and varying cell sizes.
Implementation Method 1
The scattered light then interferes with the unscattered part of the wavefront and the resulting interference pattern is recorded with a CMOS sensor
Implementation Method 2
a part of the incident wavefront originating from the light source is scattered by the sample, in this case the cell. The scattered light then interferes with the unscattered part of the wavefront and the resulting interference pattern is recorded with a CMOS sensor
Data Source
Figure 1
Figure 2
Figure 3(A)~3(E)
AI summary
The present disclosure relates to methods, systems and computer program products for detecting biological cells and/or tracing a cell lineage forest of biological cells. The tracing or detecting comprises obtaining multiple microscopy images, in particular lens-free microscopy images, depicting the biological cells. It further comprises generating a probability map for one of the microscopy images, the probability map indicating probabilities for the presence of the biological cells at multiple regions, in particular pixels, of the one microscopy image, and/or determining, based on multiple generated probability maps, one for each of the multiple microscopy images, a cell lineage forest indicating the descent and/or the localisation of at least some of the biological cells over the sequence of the multiple microscopy images.