Superpixel Image Segmentation for Digital Pathology
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Solution Overview
Problem
Image segmentation in digital pathology, particularly for biological samples, faces challenges in achieving accurate and efficient segmentation of complex images with high data density and heterogeneity, where existing methods often require extensive user interaction and may result in under or over-segmentation.
Innovation Solution
An interactive image analysis system that uses superpixels to segment digital images of biological samples by extracting feature sets from pixel intensity values, computing combined distances based on feature-set-dependent and spatial distances, and assigning unmarked superpixels to image components based on user-provided markings, allowing for semi-automatic segmentation with improved accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If larger superpixel segments are used, then richer features are provided, but under segmentation occurs
Solution Approach 1:
The patent segments the image processing task into two distinct stages: first grouping pixels into superpixels to capture rich features, then further segmenting these superpixels into final tissue regions. This two-level segmentation resolves the contradiction by preventing under-segmentation while preserving feature richness at the superpixel level.
Solution Approach 2:
The patent applies different processing qualities to different parts of the image by using superpixels as intermediate units. Each superpixel maintains its rich feature representation locally, while the global segmentation achieves precision by comparing features across superpixel boundaries using graph-based methods.
2Manufacturing precision
If smaller scale superpixel segments are used, then better boundary fit is achieved, but less discriminative features are available
Solution Approach 1:
The patent uses a hierarchical segmentation approach where superpixels serve as intermediate units between pixels and final tissue regions. This allows boundary fitting at the superpixel level while maintaining feature richness by aggregating pixel data within each superpixel, thus resolving the trade-off between boundary precision and feature discriminative power.
Solution Approach 2:
The patent merges multiple pixels into superpixels to create units that simultaneously achieve good boundary fit (by following tissue contours) and rich feature representation (by aggregating pixel information). This merging resolves the contradiction by combining the advantages of both fine-grained and coarse-grained representations.
3Manufacturing precision
If whole slide digital images are processed, then complete tissue analysis is achieved, but enormous data density and computational complexity increase
Solution Approach 1:
The patent segments the enormous whole slide image into manageable superpixel units, reducing the computational complexity from processing billions of individual pixels to processing thousands of superpixels. This segmentation enables complete tissue analysis while making the computational task tractable through the graph-based segmentation framework.
Solution Approach 2:
The patent changes the fundamental processing unit from pixels to superpixels, transforming the data representation parameters. This parameter change reduces the data density from billions of pixel values to thousands of superpixel feature vectors, making whole slide image processing computationally feasible while maintaining analysis completeness.
4Manufacturing precision
If interactive segmentation methods are used, then segmentation accuracy and flexibility are improved, but user interactions are required
Solution Approach 1:
The patent performs preliminary automatic superpixel generation and feature extraction before user interaction. This preliminary processing creates a structured representation of the image that guides subsequent user markings, reducing the number of interactions needed while maintaining high segmentation accuracy through the graph-based propagation mechanism.
Data Source
AI summary
An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.