Whole-Slide Pathology Quality Heat Maps for Artifact Detection
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Solution Overview
Problem
Digital pathology images often contain artifacts such as blur, tissue folds, pen marks, and air bubbles that interfere with accurate analysis, requiring manual and time-consuming identification by pathologists.
Innovation Solution
A computer-implemented method segments whole-slide images into tiles, applies a texture descriptor algorithm like LBP to generate artifact prediction metrics, and creates a quality heat map image to identify and filter out artifact-containing regions, enabling efficient and automated artifact detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual artifact identification is performed by pathologists, then artifact detection accuracy is maintained, but analysis time and operational complexity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computational system. A processor executes algorithms including texture descriptor algorithms (e.g., LBP) and machine learning models to automatically detect artifacts in digital pathology images, substituting the pathologist's manual visual inspection with an automated computational analysis system that operates faster and consistently
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the digital pathology image and the final diagnostic conclusion. This intermediary system processes images through multiple stages including tile-based segmentation, artifact metric generation, and quality heat map creation, providing a computational mediation layer that assists rather than completely replaces pathologist judgment
2Productivity
If automated artifact detection algorithms are implemented, then analysis speed and productivity improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex task of artifact detection into manageable segments. The image is divided into tiles, and the analysis is broken down into separate stages: texture descriptor calculation, artifact metric generation, and quality heat map creation. This segmentation allows each component to be optimized independently and processed efficiently
Solution Approach 2:
The patent implements a multi-stage filtering approach where not all images undergo the complete analysis pipeline. Quality metrics are calculated first, and only images or regions below certain quality thresholds proceed to full artifact detection analysis, reducing overall computational load while maintaining detection accuracy for problematic regions
3Reliability
If comprehensive artifact detection is performed on entire whole-slide images, then detection coverage is maximized, but processing time and computational resources increase
Solution Approach 1:
The patent segments the entire whole-slide image into smaller tile units that can be processed independently and in parallel. This allows comprehensive coverage of the entire slide while enabling efficient parallel processing, as each tile can be analyzed simultaneously without requiring sequential processing of the entire large image
Solution Approach 2:
The patent applies a two-tier processing strategy where quality metrics are computed for all tiles first, and only tiles failing quality thresholds undergo full artifact detection analysis. This partial action approach ensures comprehensive coverage of problematic regions while avoiding unnecessary processing of already-high-quality regions
Data Source
AI summary
Systems and methods relate to processing digital-pathology images. More specifically, aspects of the present disclosure are directed to accessing a whole-slide image depicting a slice of specimen, defining a set of tiles within at least part of the whole-slide image, generating one or more artifact prediction metrics by applying artifact detection to each tile of the set of tiles, wherein each of the one or more artifact prediction metrics corresponds to a predicted level of image quality of part or all of the whole-slide image, generating a quality heat map image corresponding to the whole-slide image, wherein the quality heat map image is based on the one or more artifact prediction metrics, and outputting the quality heat map image.