Sub-cellular Compartment Segmentation in Multiplexed Tissue Imaging
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Solution Overview
Problem
Existing methods for analyzing digital images of biological tissue samples lack robustness in segmenting and quantifying sub-cellular compartments, such as nuclei, membrane, and cytoplasm, and fail to account for tissue analysis at the sub-cellular level, leading to inefficiencies in cell segmentation, grouping, and quantification, especially in tissues with morphological variations and noise.
Innovation Solution
A computer-implemented method for joint segmentation and quantification of sub-cellular compartments using multiplexed images, employing a hierarchical top-down approach with topological constraints to accurately segment and quantify individual cells and their compartments, integrating contextual information from multiple channels to enhance image analysis and protein expression profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weak segmentation algorithms are used, then the processing speed is fast, but the segmentation accuracy deteriorates leading to over-segmentation or under-segmentation
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: initial weak segmentation to obtain rough cell boundaries, followed by refinement stages that improve accuracy. This multi-stage segmentation approach resolves the contradiction by achieving high accuracy without requiring a single complex algorithm to handle all cases perfectly.
Solution Approach 2:
The patent performs preliminary action by first applying weak segmentation algorithms to obtain initial cell boundary estimates, then using these results as input for subsequent refinement steps. This preliminary segmentation provides a starting point that guides more accurate but computationally intensive refinement processes, balancing speed and accuracy.
2Measurement precision
If segmentation parameters are optimized for one region, then the accuracy in that region improves, but the algorithm fails to work well in other regions with different morphology
Solution Approach 1:
The patent applies local quality by allowing segmentation parameters to vary across different regions of the image. Instead of using a single global parameter set, the system adapts parameters locally to match the morphological characteristics of each region, thereby maintaining high accuracy across diverse tissue types while preserving the effectiveness of localized optimizations.
Solution Approach 2:
The patent implements dynamics by making segmentation parameters adaptive rather than static. The system dynamically adjusts parameters based on local image characteristics and morphological variations, allowing the algorithm to respond to different tissue types and cell morphologies throughout the image, thus achieving both precision and versatility.
3Measurement precision
If traditional segmentation methods are used, then the processing is simple, but the ability to quantify sub-cellular compartments accurately deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the analysis into distinct modular components: image acquisition, initial segmentation, sub-cellular compartment identification, and quantification. This modular segmentation of the analysis pipeline enables accurate sub-cellular measurement while managing complexity through organized, separable processing stages that can be independently optimized.
Solution Approach 2:
The patent uses intermediary elements such as marker images and probability maps that facilitate the transition from simple segmentation to accurate sub-cellular quantification. These intermediaries bridge the gap between basic boundary detection and precise compartmental analysis, enabling accurate measurement without requiring the entire system to be maximally complex.
4Measurement precision
If manual intervention is used for nuclei segmentation, then the accuracy improves, but the productivity deteriorates due to time-consuming processing
Solution Approach 1:
The patent applies self-service by designing automated algorithms that perform nuclei segmentation without requiring manual intervention. The system uses self-correcting mechanisms where initial automated segmentation results are refined through iterative processing and validation, achieving accuracy comparable to manual methods while maintaining high throughput and automation.
Solution Approach 2:
The patent implements feedback by using automated validation and refinement processes that continuously improve segmentation results. The system monitors segmentation quality and adjusts parameters iteratively, providing feedback loops that maintain high accuracy while fully automating the process to preserve productivity without manual intervention.
Data Source
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Figure 2
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AI summary
Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.