Cell Image Segmentation Using Pixel Flow for Dense Nuclei
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Solution Overview
Problem
Existing image analysis techniques for biological samples face challenges in accurately identifying cell boundaries and enumerating cells due to densely packed cells, tissue heterogeneity, and the lack of reliable membrane-staining reagents, which complicates downstream analysis in automated and high-throughput imaging systems.
Innovation Solution
The use of computer-implemented methods involving a pretrained machine learning model to segment images by identifying basins and flow pixels, with pixel classification maps and direction assignments, and systems comprising imaging devices and processors for biological sample analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis techniques are used for cell segmentation, then the method is simpler, but the accuracy of identifying cell boundaries and enumerating cells deteriorates due to densely packed cells and tissue heterogeneity
Solution Approach 1:
The patent segments the image processing task into multiple components: a machine learning model generates pixel classification maps with amplitude and direction values, and a flow-based algorithm processes these classifications to identify cell boundaries. This segmentation of the overall task into specialized sub-tasks improves measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary machine learning model that processes raw images and generates pixel classification maps as intermediate representations. This intermediary layer translates complex image data into structured amplitude and direction values that the flow-based algorithm can efficiently process, thereby improving boundary identification accuracy without requiring the final algorithm to handle raw image complexity.
2Productivity
If manual cell segmentation methods are used, then the approach is more interpretable, but the productivity and throughput of analysis deteriorates
Solution Approach 1:
The patent replaces manual mechanical cell segmentation with an automated system combining machine learning and flow-based algorithms. The machine learning model automatically generates pixel classifications, and the flow algorithm automatically traces cell boundaries, eliminating manual intervention and significantly improving productivity while managing complexity through algorithmic automation.
Solution Approach 2:
The automated segmentation system is self-service in that it processes images independently without human intervention. The machine learning model self-generates pixel classification maps, and the flow algorithm self-determines cell boundaries by following amplitude gradients, enabling high-throughput automated analysis of biological samples.
3Reliability
If membrane-staining reagents are used to improve cell boundary visibility, then the reliability of segmentation improves, but the ease of manufacture and cost deteriorates due to lack of compatible reagents
Solution Approach 1:
The patent extracts the cell boundary detection function from chemical staining reagents and implements it through computational image analysis. Instead of relying on membrane-staining reagents to provide contrast, the machine learning model learns to detect boundaries directly from the image data, and the flow algorithm extracts boundary information from amplitude gradients, eliminating the need for specialized staining reagents and simplifying sample preparation.
Data Source
AI summary
Provided herein are methods for image segmentation. An image of a sample having a plurality of nuclei is received. The image comprises a plurality of pixels arranged in a first dimension and a second dimension. The plurality of pixels indicates a signal from a nuclear stain of the plurality of nuclei. For each pixel of the plurality of pixels, a pixel classification is determined, thereby generating a pixel classification map corresponding to the image. Determining the pixel classification comprises determining a first classification of the pixel corresponding to the first dimension and determining a second classification of the pixel corresponding to the second dimension. A segmentation mask is determined based on the pixel classification map.