Immunofluorescence Cell Segmentation Using Membrane and Nuclear Cues
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Solution Overview
Problem
Existing techniques for whole-area cell segmentation in tissue specimens are limited by manual interaction, computational intensity, and lack of direct applicability from cell culture assays to tissue assays, particularly in fluorescence tissue microscopy, leading to insufficient cell boundary detection and irregular spatial distributions.
Innovation Solution
A method involving membrane and nuclear staining followed by automated image processing, including membrane segmentation and nuclear seed detection, to generate a labeled fluorescence image of cells, using texture- and kernel-based image processing algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual seed selection is used for cell segmentation, then segmentation accuracy can be maintained for complex cell shapes, but automation is lost and user interaction is required
Solution Approach 1:
The system performs automated nuclear seed detection by analyzing fluorescence images to identify cell nuclei positions without requiring manual user input. The algorithm automatically processes the image data, detects nuclear regions, and generates seed points for subsequent cell segmentation, enabling the system to serve itself rather than requiring external manual operation.
Solution Approach 2:
The patent replaces the manual mechanical interaction of users clicking to select seeds with an automated image processing system. The system uses fluorescence image analysis and algorithms to automatically detect and locate cell nuclei, substituting the mechanical user action with an automated computational process that achieves the same functional outcome.
2Measurement precision
If contour-finding techniques are used for cell boundary detection, then cell boundaries can be identified, but computational time increases and convergence becomes slow
Solution Approach 1:
The system performs preliminary nuclear segmentation and seed detection before conducting cell boundary detection. By first identifying nuclear regions and generating seed points, the system prepares the data structure and initial parameters needed for subsequent cell segmentation, avoiding the need for slow iterative contour-finding techniques and enabling faster processing.
3Productivity
If nuclear segmentation alone is performed, then nuclei locations can be localized, but full cell segmentation including cell boundaries cannot be achieved
Solution Approach 1:
The patent divides the cell segmentation process into two distinct segments: nuclear segmentation to locate cell nuclei, and cell segmentation to identify full cell boundaries. The nuclear segmentation step provides seed points that guide the subsequent cell segmentation process, allowing the system to efficiently progress from nuclear localization to complete cell boundary detection without attempting to perform both tasks simultaneously.
Solution Approach 2:
The system uses nuclear segmentation results as an intermediary step between image acquisition and final cell segmentation. The detected nuclear regions serve as intermediate data that guides the cell segmentation algorithm, providing essential starting information that bridges the gap between raw images and accurate cell boundary detection.
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 fully automated whole-cell segmentation in tissue specimens, improving accuracy and efficiency by extending cell boundary detection beyond nuclear localization, especially in cases with complex shapes and irregular distributions.
Implementation Method 1
a membrane stain is applied to a tissue sample... A first fluorescence image is obtained of at least a portion of the membrane-stained sample
Implementation Method 2
The tissue sample is stained with a nuclear stain such that nuclei of the plurality of cells are stained... A second fluorescence image is obtained of at least a portion of the nuclear-stained sample
Data Source
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AI summary
Various techniques are provided for performing automated full-cell segmentation and labeling in immunofluorescent microscopy. These techniques perform membrane segmentation and nuclear seed detection separate and independently from each other, then combine their results to identify cell boundaries. Some embodiments use texture- and kernel-based image processing to perform the method. In some embodiments, the method for obtaining membrane features disclosed herein can be used in conjunction with or separate from the nuclear features. The results can be used for a variety of purposes, including whole-area cell segmentation in fluorescence-based tissue imaging.