Dual-Mode Cell Event Detection via Lensless Imaging and AI
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Solution Overview
Problem
Current methods for observing cell division or cell death, such as microscopy and optical methods using viability markers, face challenges in efficiently monitoring large samples and predicting cell events across extended areas.
Innovation Solution
A method involving a dual-mode observation device that uses lensless imaging and conventional microscopy, combined with a supervised artificial intelligence algorithm, to detect and predict cell events by analyzing optical path differences and absorbance patterns in samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microscopy is used to observe cell division, then high resolution is achieved, but the observation field is restricted
Solution Approach 1:
The system segments the observation process into two distinct modes: lensless imaging mode for wide-field survey and microscopy mode for detailed observation. The control unit manages switching between these modes, allowing the system to first identify regions of interest in a large field using lensless imaging, then apply high-resolution microscopy only to those specific areas, thus resolving the contradiction between wide observation field and high resolution
Solution Approach 2:
The system adds the dimension of temporal sequencing to the observation process. Instead of attempting to capture both wide field and high resolution simultaneously in space, the system sequences the observation in time: first wide-field lensless imaging to map the sample, then targeted microscopy on identified cell events. This temporal dimension allows the system to achieve both wide coverage and high resolution without physical contradiction
2Measurement precision
If manual observation of cell events is performed, then detailed analysis is possible, but extensive time and manual effort are required
Solution Approach 1:
The system implements self-service through automated identification and tracking of cell events. The lensless imaging mode automatically surveys the entire sample field, the control unit automatically identifies regions containing cell events, and the system automatically switches to microscopy mode for those specific areas. This eliminates the need for manual scanning and identification, allowing detailed analysis of cell events without requiring extensive manual effort or time
Solution Approach 2:
The system replaces the mechanical process of manual observation with an automated optical-electronic system. Instead of a researcher manually examining samples under a microscope, the system uses lensless imaging technology combined with automated control logic to identify and track cell events, substituting human manual labor with an automated detection and control mechanism that operates continuously without fatigue or time constraints
3Area of stationary object
If lensless imaging is used, then wide observation field is achieved, but resolution is reduced compared to microscopy
Solution Approach 1:
The system merges two previously separate observation technologies - lensless imaging and microscopy - into a single integrated system. The lensless imaging unit provides wide-field coverage while the microscopy unit provides high resolution. The control unit coordinates these two modes, allowing them to work together complementarily: lensless imaging identifies where to look, and microscopy provides detailed views of those specific locations, thus combining the advantages of both approaches while mitigating their individual limitations
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 automatic identification and counting of cell events across large samples, allowing for precise observation and prediction of cell divisions and deaths without the need for extensive manual analysis.
Implementation Method 1
calculating an observation image at each instant, the observation image corresponding to a spatial distribution of an optical path difference induced by the cells, or of an absorbance of the cells
Implementation Method 2
calculating an observation image at each instant, the observation image corresponding to a spatial distribution of an optical path difference induced by the cells, or of an absorbance of the cells
Data Source
AI summary
Method for detecting or predicting the occurrence of a cell event, selected from between cell division or cell death, in a sample (10), the sample extending in at least one sample plane (P10) and comprising cells (10p) immersed in a medium, the method comprising the following steps:a) arranging the sample between a light source (11) and an image sensor (16);b) illuminating the sample (10) using the light source (11) and acquiring a plurality of successive images ((I1(ti), I1(ti+1) . . . I1(ti+K)) of the sample at different instants ([ti; ti+K]) forming an acquisition time range, each image being representative of an exposure light wave to which the image sensor is exposed;c) on the basis of each acquired image of the sample, calculating an observation image at each instant (I10(ti), I10(ti+1) . . . I10(ti+K)), the observation image corresponding to a spatial distribution of an optical path difference induced by the cells, or of an absorbance of the cells, in the sample plane (P10);d) using the observation images resulting from c) as input data of a supervised artificial intelligence algorithm (CNNd, CNNp), so as to predict or locate an occurrence of the cell event.