Automated Heat Map for Digital Pathology FOV Selection
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Solution Overview
Problem
Manual selection of Fields of View (FOVs) in digital pathology is subjective and biased, leading to variability in clinical diagnostic scores, as existing automated methods fail to accurately identify weakly positive or small tumor regions and are prone to selecting non-specific staining artifacts as hot spots.
Innovation Solution
A semi-automated workflow that generates a heat map based on specific scoring criteria, correlating pixel values to tumor and staining type, to identify and rank potential tumor hot spots, reducing bias and improving FOV selection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of hot spot regions is performed by pathologists, then clinical diagnostic experience and judgment are utilized, but variability in score selection occurs and reproducibility decreases
Solution Approach 1:
The patent replaces the manual mechanical process of visual inspection and score selection by pathologists with an automated image analysis algorithm. The algorithm objectively identifies hot spot regions and determines scores based on quantitative image analysis, eliminating human variability while maintaining diagnostic accuracy through programmed scoring criteria.
Solution Approach 2:
The system enables self-service by allowing the image analysis algorithm to autonomously perform hot spot identification and score selection without requiring manual intervention. The algorithm processes the digitized tissue image, identifies regions of interest, and generates scores automatically, making the diagnostic process independent of individual pathologist preferences.
2Reliability
If automated image analysis algorithms are used to detect tumor cells and quantify biomarker expression, then objectivity and consistency are improved, but difficulty in detecting weakly positive or small tumor regions increases
Solution Approach 1:
The patent implements a dynamic, multi-stage image analysis process that adapts to different staining intensities and tissue characteristics. The algorithm adjusts its detection parameters and processing steps based on the specific features of each image, enabling it to effectively identify both strongly and weakly positive regions while maintaining consistent scoring across diverse samples.
Solution Approach 2:
The system performs preliminary image processing and enhancement steps before the main detection and scoring phases. This includes preprocessing operations that amplify subtle staining patterns and improve contrast, allowing the algorithm to detect weakly positive regions that would otherwise be difficult to identify, thereby improving both sensitivity and consistency.
3Measurement precision
If pathologists visually review entire digitized whole slide images to identify tumor regions and select hot spots, then comprehensive assessment is achieved, but time consumption and operational complexity increase
Solution Approach 1:
The patent segments the large-scale whole slide image into smaller, manageable regions and processes them through the image analysis algorithm. This segmentation approach allows the system to efficiently analyze comprehensive tissue areas without requiring manual review of entire slides, significantly reducing time while maintaining accurate hot spot identification through systematic region-by-region evaluation.
Solution Approach 2:
The patent replaces the time-consuming manual visual review process with automated image analysis. The algorithm rapidly processes digitized whole slide images, identifying tumor regions and hot spots through computational methods that are both faster and more consistent than human visual inspection, thereby eliminating the time loss associated with manual review while preserving identification accuracy.
4Productivity
If existing automated methods select hot spots based on general staining patterns, then processing speed is improved, but accuracy in identifying true tumor hot spots decreases due to selection of non-specific artifacts
Solution Approach 1:
The patent incorporates feedback mechanisms in the image analysis algorithm that continuously evaluate detected regions against multiple criteria. The system uses feedback from intermediate analysis results to refine its identification process, distinguishing true tumor hot spots from non-specific artifacts by comparing detected features against established patterns and scoring thresholds, thereby improving accuracy while maintaining processing speed.
Solution Approach 2:
The patent employs multiple parameter-based filtering and validation steps in the automated analysis process. By changing and adjusting various analysis parameters—such as staining intensity thresholds, region size criteria, and pattern recognition parameters—the system accurately distinguishes true tumor hot spots from artifacts, achieving both high processing speed and precise identification through optimized parameter settings.
Data Source
AI summary
Methods and systems for generating a heat map that reduces bias in selecting FOVs are disclosed. Some disclosed methods include annotating a primary stained image, registering the annotation to a secondary serial specific stained image, using an image analysis algorithm to compute a scoring criteria specific to the tissue and assay type for tiled regions in the image, using a sliding window in the annotated tumor region to compute values for each pixel in a heat map which correlate to the specific scoring criteria, displaying the heat map at low resolution, ranking and selecting hot spots, selecting FOVs from the hot spot regions which results in displaying the slide-level score for the FOVs. The systems comprise, among other things, software configured to perform the referenced method.