Automated Whole-Slide Image Quality Control via Density Heat Maps
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Solution Overview
Problem
Current whole-slide analysis methods in digital pathology are inefficient as they require manual review of numerous fields-of-view (FOVs) to detect histopathological artefacts, leading to incomplete quality control and tedious processes, where errors in tissue regions not visible in selected FOVs can be missed.
Innovation Solution
An image processing system that automatically selects meaningful regions on a whole-slide image by generating heat maps to quantify local object densities, applying rules to identify candidate FOVs for quality control, and allowing observers to review only the most scrutinized regions, thereby excluding artefacts from analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of numerous FOVs is performed to detect histopathological artefacts, then quality control completeness is improved, but time consumption and operational tediousness increase
Solution Approach 1:
The whole-slide image is divided into multiple fields-of-view (FOVs), and the system selectively presents only certain FOVs for manual review based on automated analysis. This segmentation allows comprehensive quality control without requiring review of all FOVs, thus reducing time consumption while maintaining reliability.
Solution Approach 2:
An automated image analysis algorithm acts as an intermediary between the whole-slide image and the observer. The algorithm pre-processes the image to identify and flag suspicious regions, serving as a mediator that guides the observer's attention to critical areas, thereby reducing the time needed for manual quality control while maintaining completeness.
2Ease of operation
If random or systematic sampling of FOVs is used for quality control, then review process is simplified, but detection of errors in non-selected regions is lost
Solution Approach 1:
The automated image analysis algorithm serves as an intelligent intermediary that selects which FOVs to present for review based on content analysis. Instead of random or systematic sampling, the algorithm identifies and flags FOVs containing suspicious features or potential artefacts, ensuring that the simplified review process still achieves comprehensive error detection.
Solution Approach 2:
The system changes the selection criterion from random or systematic parameters to content-based parameters. FOVs are selected for review based on their visual content and features detected by the algorithm, such as unusual patterns or artefact characteristics, rather than their position or random selection, thereby maintaining both simplicity and reliability.
3Adaptability or versatility
If interactive FOV selection through GUI is implemented, then user control over region selection is improved, but operational tediousness increases
Solution Approach 1:
The system performs automatic FOV selection and presentation based on algorithmic analysis of the whole-slide image. The automated system serves itself by identifying and flagging suspicious regions without requiring manual user intervention for FOV selection, thereby maintaining adaptability while significantly reducing operational tediousness.
Solution Approach 2:
The automated image analysis is performed in advance to identify and flag suspicious FOVs before they are presented to the user. This preliminary action of pre-processing and selection eliminates the need for tedious interactive selection during the review process, while still allowing user control over the final selection of FOVs for detailed examination.
Data Source
AI summary
The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.