Pathology Image Processing Using Adaptive Resolution Selection
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Solution Overview
Problem
Existing systems for processing pathology images are often slow, costly, and inefficient due to the large size and complexity of these images, which can limit their effective analysis and manipulation.
Innovation Solution
A method and system that utilize multiple representations of pathology image regions, with smaller data size alternatives, to determine when a lower-resolution representation can be used in place of a higher-resolution one based on specific image property criteria, leveraging machine learning models like convolutional neural networks for efficient processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher-resolution representations are used for pathology image regions, then image quality and detail are improved, but processing speed and computational efficiency deteriorate
Solution Approach 1:
The pathology image is divided into multiple regions of interest (ROIs), and each ROI is represented at different resolutions. The system segments the image into regions that require high resolution and regions that can use lower resolution, allowing parallel processing of multiple representations simultaneously.
Solution Approach 2:
The system dynamically selects between first and second representations based on processing needs and resource availability. The selection is not static but adapts based on the specific analysis task, allowing the system to optimize between quality and speed in real-time.
2Measurement precision
If higher-resolution representations are used for pathology image regions, then image detail is improved, but storage requirements and computational resources worsen
Solution Approach 1:
The image data is segmented into multiple representations stored separately. Instead of storing one large high-resolution image, the system stores multiple smaller representations at different resolutions, reducing total storage requirements while maintaining access to high-detail regions when needed.
Solution Approach 2:
Different regions of the pathology image are assigned different quality levels. Regions containing critical diagnostic features are maintained at high resolution, while other regions use lower resolution representations, optimizing the balance between detail preservation and storage efficiency.
3Productivity
If multiple representations of image regions are maintained, then processing efficiency is improved, but system complexity worsens
Solution Approach 1:
The system pre-processes pathology images into multiple representations before actual analysis begins. This preliminary action creates a hierarchy of representations that can be quickly selected and combined during processing, avoiding the need to generate multiple resolutions on-demand and reducing real-time computational complexity.
4Productivity
If lower-resolution representations are used, then processing cost and computational resources are reduced, but image quality deteriorates
Solution Approach 1:
The system segments the analysis task into regions requiring high resolution and regions where lower resolution suffices. By processing only critical regions at high resolution, the system maintains diagnostic quality where needed while reducing overall processing costs and computational resources.
Data Source
AI summary
A method of facilitating processing of pathology images involves receiving pathology image data representing a pathology image having a plurality of image regions, wherein the pathology image data includes, for each of the plurality of image regions, a respective plurality of representations of the image region including a first representation and a second representation, the second representation having a smaller data size than the first representation. The method involves, for each of the plurality of image regions: determining, based at least in part on the first representation of the image region, a first set of image properties, determining whether the first set of image properties meets first image property criteria, and, if the first set of image properties meets the first image property criteria, producing signals for causing the second representation to be used in place of the first representation. Other methods, systems, and computer-readable media are disclosed.