Pixelwise descriptors for cancer tissue image analysis
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Solution Overview
Problem
Current computer-assisted image analysis systems for cancer diagnosis are inefficient due to the computational intensity of object-oriented analysis, which is necessary for accurate cancer scoring and grading, especially when dealing with large digital pathology datasets like gigapixels of tissue slides, leading to slower processing times.
Innovation Solution
The method combines object-oriented and pixel-oriented analysis by using pixelwise descriptors and decision trees to segment and classify pixels without fully segmenting the image into objects, generating a pixel heat map that assigns colors based on object classes, thereby reducing computational load while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object-oriented analysis is used to segment and classify pixels for accurate cancer scoring, then measurement precision is improved, but processing time increases significantly
Solution Approach 1:
The image is divided into superpixels (groups of adjacent pixels) that are then clustered into object classes. This segmentation approach allows the system to process images at a coarser granularity level while maintaining the ability to identify cancerous regions accurately, thereby reducing processing time without sacrificing measurement precision.
Solution Approach 2:
The system performs object-oriented analysis on only a portion of the image (selected superpixels or regions of interest) rather than the entire gigapixel image. This partial action approach maintains accuracy for cancer scoring while significantly reducing the computational burden and processing time.
2Measurement precision
If full object-oriented analysis is performed on gigapixel images, then classification accuracy is improved, but computational resources are excessively consumed
Solution Approach 1:
The gigapixel image is segmented into superpixels and then further clustered into object classes. This hierarchical segmentation reduces the number of individual pixels that need to be processed, thereby reducing computational resource consumption while maintaining classification accuracy through the preservation of spatial and contextual relationships.
Solution Approach 2:
The system creates simplified representations (superpixels and object class labels) that copy the essential structural and contextual information from the original pixel data. These copied representations can be processed more efficiently while retaining the information needed for accurate cancer classification.
3Productivity
If pixel-oriented analysis is used instead of object-oriented analysis, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments pixels into superpixels and then clusters these superpixels into object classes. This approach combines the speed of pixel-oriented analysis with the accuracy benefits of object-oriented analysis, as the segmentation preserves local spatial relationships while enabling faster processing through reduced computational complexity.
Solution Approach 2:
The system creates a composite analysis approach that combines pixel-oriented processing (for speed) with object-oriented classification (for accuracy). By integrating both methods through superpixel formation and clustering, the system achieves both high processing speed and high measurement precision for cancer scoring.
Data Source
AI summary
Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel.