Cell Segmentation via Nuclear Staining and mFISH Image Analysis
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Solution Overview
Problem
Current methods for visualizing and quantifying biological analytes in tissues, such as multiplexed fluorescence in-situ hybridization (mFISH) imaging, face challenges in accurately segmenting cells with complex structures like brain tissue, where extracellular matrices and fine features like dendrites are difficult to preserve, leading to poor signal, high variability, and increased processing time.
Innovation Solution
A cell detection system that uses fluorescent in-situ hybridization images and nucleus-stained images to enhance cell features, allowing for cell segmentation without membrane staining, thereby preserving intricate structures and reducing processing time while improving signal quality and variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If membrane staining is used to segment cells in complex tissues, then cell boundaries can be visualized, but processing time increases and fine features like dendrites are lost
Solution Approach 1:
The patent extracts and removes the membrane staining step from the traditional cell segmentation workflow. Instead of using membrane stains to define cell boundaries, the method directly segments cells based on nuclear positions and expands these segments to define cell territories, thereby eliminating the time-consuming staining and imaging of cell membranes while preserving segmentation accuracy.
Solution Approach 2:
The patent performs preliminary nuclear detection and segmentation before any cell boundary definition. By first identifying nuclear positions through fluorescent staining and then using computational expansion to define cell territories, the method establishes cell segments in advance without requiring subsequent membrane visualization steps, thus reducing processing time.
2Measurement precision
If membrane staining is used to segment cells, then cell boundaries are defined, but fine features like dendrites and tubules are lost
Solution Approach 1:
The patent removes the membrane staining component from the segmentation process, thereby eliminating the loss of fine structural features that occurs when membranes are stained and imaged. The method preserves fine features like dendrites and tubules by defining cell boundaries through computational expansion from nuclear positions rather than through physical membrane visualization.
Solution Approach 2:
The patent replaces the mechanical/chemical process of membrane staining and physical boundary definition with a computational approach. By using algorithms to expand from nuclear positions and define cell territories, the method substitutes the physical membrane staining mechanism with an information-processing approach that preserves fine structural details.
3Shape
If traditional segmentation methods are used in complex tissues, then cell structures can be visualized, but signal quality decreases and variability increases
Solution Approach 1:
The patent introduces computational algorithms as an intermediary between nuclear detection and cell boundary definition. This intermediary process uses controlled expansion from nuclear positions to generate cell segments, providing a reliable and consistent method that maintains signal quality while visualizing cell structures in complex tissues.
Solution Approach 2:
The patent changes the fundamental parameter used for segmentation from membrane intensity to nuclear position-based expansion. By shifting from staining intensity measurements to spatial expansion algorithms, the method improves reliability and reduces variability while maintaining the ability to visualize cell structures.
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 accurate cell segmentation and feature extraction in complex tissues like brain tissue, providing improved signal quality, reduced variability, and faster processing times compared to traditional methods, while maintaining the morphology and features of cells, including tubules and other structures.
Implementation Method 1
These probes have different labeling schemes that will allow one to distinguish different RNA or DNA nucleotide segments when the complementary, fluorescent labeled probes are introduced to the sample. Then the sequential rounds of fluorescence images are acquired with exposure to excitation light of different wavelengths.
Data Source
AI summary
Detecting cells depicted in an image using RNA segmentation can include obtaining a FISH image of a tissue that depicts multiple cells, obtaining a nuclear stained image of the tissue, and generating a mask that includes multiple areas that each have a position with respect to the tissue by enhancing structures depicted in the FISH image. Edges depicted in the enhanced FISH image are detected to use for the mask, and positions are determined for a first plurality of regions that fit potential nuclei depicted in the nuclear stained image. A second plurality of regions are selected from the first plurality by determining, using the mask, which regions from the first plurality overlap with the position of an area from multiple areas in the mask. Unique nuclei in the tissue are labelled using the second plurality of regions that each indicate a potential nuclei in the tissue.