Cell Image Segmentation Using Morphological Nucleus and Contour Maps
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Solution Overview
Problem
Existing cell image segmentation methods, particularly those using deep learning models, lack robustness due to the need for manual data labeling and are sensitive to changes in imaging environments, leading to inefficiencies and increased costs.
Innovation Solution
A morphology-based method for cell image processing that includes obtaining a target cell nucleus map, region contour map, and contour map using morphological operations, enabling segmentation without manual data labeling and maintaining robustness across varying imaging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for cell image segmentation, then segmentation accuracy can be improved, but the method becomes sensitive to imaging environment changes and requires manual data labeling
Solution Approach 1:
The patent divides the cell image segmentation process into multiple stages: initial segmentation to obtain candidate regions, followed by refinement through morphological operations to extract precise cell boundaries and nuclear regions. This multi-stage segmentation approach maintains accuracy while improving robustness by separating the segmentation task into manageable steps that can be independently optimized.
Solution Approach 2:
The patent implements automatic quality assessment and iterative optimization where the system evaluates its own segmentation results and automatically adjusts parameters without manual intervention. The quality assessment module analyzes segmentation outcomes and feeds back to optimize the segmentation process, enabling the system to self-improve and maintain robustness across different imaging conditions.
2Measurement precision
If manual data labeling is performed for deep learning model training, then model accuracy can be improved, but production costs and time consumption increase
Solution Approach 1:
The patent performs preliminary automatic segmentation using morphological operations to generate initial segmentation results and quality assessments before any manual labeling. This preliminary action creates a foundation that reduces the amount of manual labeling needed, as the system can automatically process many images without human intervention and only requires minimal manual correction for edge cases.
Solution Approach 2:
The patent implements a feedback mechanism where segmentation results are automatically quality-assessed and used to iteratively optimize the segmentation process. This feedback loop allows the system to learn from its own results and improve automatically, reducing dependency on manually labeled training data while maintaining high accuracy.
3Productivity
If complex deep learning models are deployed for cell image processing, then segmentation performance can be improved, but system complexity and computational costs increase
Solution Approach 1:
The patent replaces complex deep learning mechanical systems with morphological mathematical operations. By using morphological operations (erosion, dilation, opening, closing) based on mathematical morphology theory, the system achieves effective segmentation without requiring complex neural network architectures, thereby reducing computational complexity while maintaining productivity.
Solution Approach 2:
The patent optimizes segmentation performance by adjusting morphological operation parameters such as structuring element size, shape, and iteration counts rather than increasing model complexity. This parameter-based optimization allows the system to achieve high productivity through simple, interpretable operations with tunable parameters, avoiding the need for complex deep learning models.
Data Source
AI summary
The present disclosure provides a method for processing a cell image, an electronic device, and a storage medium. The method includes: obtaining, based on a morphological method, a target cell nucleus map according to an initial image segmentation result of a target cell image, where the target cell image is an image corresponding to at least one cell; obtaining, based on the morphological method, a target cell region contour map according to the target cell nucleus map and the initial image segmentation result; obtaining, based on the morphological method, a target cell contour map according to the target cell region contour map and the target cell nucleus map; and obtaining a target image segmentation result according to the target cell contour map and the target cell nucleus map.