3D Cell Image Processing With Integrated Spatial Analysis
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Solution Overview
Problem
Processing 3D multiplexed cell images requires complex and advanced image processing methods due to increased dimensionality, often necessitating multiple separate tools for segmentation, classification, and data analysis, which can lead to errors and inefficiencies.
Innovation Solution
A method that segments, classifies, and derives spatial information from 3D image representations, allowing for streamlined processing and visualization of biological samples by integrating these steps into a single tool, including features like region of interest definition, channel grouping, and distance relation measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate tools are used for segmentation, classification, and data analysis of 3D multiplexed images, then comprehensive processing capability is achieved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent combines segmentation, classification, and spatial data analysis functions into a single integrated software platform. The system processes 3D multiplexed images through unified computational workflows that perform multiple operations sequentially without requiring separate tools, thereby reducing system complexity while maintaining comprehensive processing capability.
Solution Approach 2:
The software platform is designed to perform multiple functions including segmentation of cellular structures, classification of objects based on spatial and intensity features, and derivation of spatial information. This multi-functional approach allows a single tool to replace multiple specialized tools while maintaining versatility in handling different image processing tasks.
2Adaptability or versatility
If multiple separate tools are used for processing steps, then specialized functionality is achieved, but ease of operation and productivity worsen
Solution Approach 1:
The system merges multiple processing steps into a single integrated workflow where segmentation, classification, and spatial analysis are performed sequentially within one software environment. This eliminates the need for users to manually transfer data between multiple specialized tools, significantly improving ease of operation while preserving specialized functionality through modular algorithm design.
3Measurement precision
If manual processing by domain experts is performed, then analysis accuracy is improved, but productivity and time consumption worsen
Solution Approach 1:
The system implements automated algorithms for segmentation and classification that perform processing without requiring manual intervention by domain experts. The computational methods automatically derive spatial information and generate quantitative results, maintaining high accuracy through sophisticated image processing techniques while dramatically increasing productivity by eliminating time-consuming manual analysis steps.
4Loss of information
If comprehensive data processing is performed on entire 3D representations, then analysis completeness is improved, but computational complexity and processing time worsen
Solution Approach 1:
The system segments the 3D multiplexed image representation into distinct cellular structures and objects before performing classification and spatial analysis. This segmentation approach allows the software to process data in manageable units, maintaining analysis completeness by preserving spatial relationships while reducing overall computational complexity and processing time compared to analyzing the entire volume as a single entity.
Data Source
AI summary
A method for processing an image of a sample. The method comprises receiving a 3D representation of the sample as an input, where the 3D representation comprising a plurality of input channels. The method further comprises segmenting the 3D representation 120 by segmenting and partitioning individual objects within the 3D representation. The method further comprises classifying the objects in the segmented 3D representation. The method further comprises deriving spatial information based on the classification of the objects. Deriving spatial information comprises performing a distance relation measurement between the objects. The method comprises at least one of analyzing the spatial information or triggering a visualization of the spatial information.