Multiple-Stain Image Segmentation in Dense, Heterogeneous Tissue
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Solution Overview
Problem
Existing methods face challenges in accurately segmenting cells in biological samples due to dense packing, tissue heterogeneity, and lack of reliable staining reagents, which complicates downstream analysis such as cell enumeration and spatial resolution.
Innovation Solution
A computer-implemented method using multiple stain indicators and machine learning models to generate nuclear and cellular segmentation masks, expanding from nuclei based on amplitude and divergence values to predict cell regions, employing geodesic and isometric expansion models to enhance cell segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cell segmentation methods are used, then processing speed may be maintained, but segmentation accuracy deteriorates due to dense cell packing and tissue heterogeneity
Solution Approach 1:
The patent divides the cell segmentation task into multiple independent stages: nuclear segmentation using one stain, cytoplasmic segmentation using another stain, and boundary refinement. This multi-stage segmentation approach improves accuracy by addressing different cellular components separately rather than attempting to segment entire cells in a single step, directly resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent employs multiple staining parameters (different fluorophores, excitation wavelengths, and emission filters) to capture distinct cellular features. By changing the optical parameters between nuclear and cytoplasmic staining channels, the system achieves better segmentation accuracy while managing complexity through systematic parameter variation rather than complex algorithmic changes.
2Measurement precision
If multiple stain indicators are used, then cell structure identification accuracy improves, but analysis time and processing complexity increase
Solution Approach 1:
The patent performs nuclear segmentation first using nuclear stains, establishing accurate nuclear boundaries before proceeding to cytoplasmic segmentation. This preliminary action on the more easily identifiable nuclei provides a foundation for subsequent cytoplasmic boundary detection, reducing overall analysis time by breaking the complex task into preparatory and refinement stages.
Solution Approach 2:
The patent uses nuclear segmentation results as an intermediary step that mediates between the two staining channels. The accurately segmented nuclei serve as a reference framework that guides and constrains the cytoplasmic segmentation process, enabling faster processing by providing pre-established boundaries that reduce the computational search space for cytoplasmic boundaries.
3Measurement precision
If multiple staining channels are processed, then cell boundary detection accuracy improves, but computational load and processing complexity increase
Solution Approach 1:
The patent segments the computational processing into distinct modules: nuclear channel processing, cytoplasmic channel processing, and boundary integration. Each module handles specific computational tasks independently, improving boundary detection accuracy through specialized processing while managing complexity by avoiding monolithic algorithm design.
Solution Approach 2:
The patent merges the results from multiple staining channels through a unified segmentation framework that integrates nuclear and cytoplasmic boundaries. This merging approach improves overall boundary detection accuracy by combining information from different stains while managing processing complexity through systematic integration rather than handling each channel completely separately throughout the entire pipeline.
Data Source
AI summary
In embodiments, a method includes reading a nuclear segmentation mask of an image including pixels arranged in two dimensions. The nuclear segmentation mask identifies cellular nuclei stained with a cellular nucleus stain. The method includes determining amplitudes for each pixel. Each amplitude corresponds to exactly one dimension of the two dimensions. The method includes constructing a graph having nodes and edges. Each node corresponds to a pixel. Nodes corresponding to neighboring pixels are connected by an edge. The method includes assigning a weight to each edge. Each edge's weight is based on amplitudes of pixels corresponding to nodes connected thereby. The method further includes, based on the graph, determining, for each cellular nuclei, a heat map corresponding to a predicted cell region associated with that cellular nucleus. The method further includes based on the heat maps of the cellular nuclei, determining a cellular segmentation mask comprising predicted cell regions.