3D Cell Observation System with Radial Region Segmentation
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Solution Overview
Problem
Current observation systems struggle to accurately analyze specific regions within three-dimensional structures, such as cell clusters or nuclei, due to limitations in recognizing and distinguishing cell components in 3D images.
Innovation Solution
An observation system that utilizes a processor to recognize the 3D shape of a fluorescent specimen, sets a similar region with a fixed radial distance from the circumscribed surface, and identifies cells or components within this region, allowing for precise analysis and display of cells inside and outside the region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D fluorescence observation images are used to analyze cell clusters, then the observation system can recognize cell components and measure their areas, but the system cannot accurately analyze specific regions at the outer or inner periphery of three-dimensional structures
Solution Approach 1:
The patent transitions from 2D fluorescence observation images to 3D confocal microscopy images, adding the depth dimension to enable accurate analysis of specific regions at the outer or inner periphery of three-dimensional cell clusters. This dimensional change allows the system to distinguish between different radial regions that were indistinguishable in 2D projections.
Solution Approach 2:
The patent segments the three-dimensional cell cluster into distinct radial regions (outer periphery, inner periphery, and center) by calculating the distance from the center of gravity. This segmentation enables selective analysis of specific regions while maintaining the overall 3D structure context, resolving the contradiction between analysis precision and system complexity.
2Measurement precision
If the entire 3D structure is analyzed without region selection, then the observation system provides comprehensive data, but it cannot distinguish between cells at different radial positions for evaluating drug effects or toxicity
Solution Approach 1:
The patent performs preliminary calculation of the center of gravity and radial distance for each cell before analysis. This preliminary action enables subsequent selective analysis of outer or inner periphery regions without requiring complex manual intervention, maintaining ease of operation while achieving region-specific precision for drug effect or toxicity evaluation.
Solution Approach 2:
The patent applies different analysis criteria to different radial regions of the cell cluster. By calculating the distance from the center of gravity, the system automatically identifies and applies appropriate analysis parameters to outer periphery cells versus inner periphery cells, enabling region-specific analysis while keeping the overall operation simple through automated classification.
Data Source
AI summary
A fixed region at an outer periphery or an inner periphery of a sample that has a three-dimensional structure is selectively analyzed with accuracy. Provided is an observation system including a CPU that recognizes the 3D shape of an observation target, such as a spheroid, from a 3D image of cells, that sets a 3D mask of which the radial distance from a circumscribed surface of the recognized 3D shape is fixed over the entire region of the circumscribed surface and of which the shape is similar to the circumscribed surface, and that identifies a cell contained inside the set 3D mask.