Whole-Slide Segmentation Ground Truth from Multi-Stain Annotation Refinement
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Solution Overview
Problem
Training machine-learning algorithms for whole-slide images in digital pathology is time-consuming and error-prone due to the high cost and complexity of obtaining high-resolution ground-truth labels, especially for rare and difficult-to-detect samples, and existing active learning approaches do not adequately address the diversity and specificity required for diverse tissue types.
Innovation Solution
A method for refining initial segmentations of whole-slide images by generating a second segmentation with higher detail using automated annotations, combining different histopathological stains and image processing techniques to create a training data set that reduces annotation effort and ensures accurate training of a machine-learned segmentation algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution ground-truth labels are obtained through expert annotation, then training accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by using weak supervision to pre-generate annotations before expert refinement. The system first creates initial ground-truth labels through automated algorithms and only then applies expert annotation to correct errors, rather than requiring experts to annotate everything from scratch. This reduces the overall annotation time while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary automated annotation system that acts as a bridge between weak supervision data and expert annotations. This intermediary generates preliminary annotations that are then refined by experts, reducing the direct burden on experts while maintaining high-quality ground-truth labels.
2Reliability
If comprehensive annotations are provided for all training samples, then model performance is improved, but annotation cost increases
Solution Approach 1:
The patent applies local quality by focusing expert annotation efforts only on difficult or ambiguous regions of the images rather than uniformly annotating all areas. The system identifies regions that require high-quality annotations and directs expert attention there, while easier regions are handled by automated methods. This maintains model performance while reducing overall annotation effort.
Solution Approach 2:
The patent uses partial action by providing comprehensive annotations only for a subset of training samples rather than all samples. The system strategically selects which samples require detailed expert annotations based on their difficulty or representativeness, achieving good model performance with reduced annotation effort compared to annotating every sample exhaustively.
3Measurement precision
If multiple histopathological stains are processed, then tissue type identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex multi-stain processing task into separate independent modules, each handling a specific stain type. The system processes each histopathological stain through dedicated processing pipelines and then integrates the results, making the overall complex process more manageable and maintainable while preserving high identification accuracy.
Solution Approach 2:
The patent implements universality by creating a unified processing framework that can handle multiple different histopathological stain types through a common architecture. The system uses a universal processing pipeline that adapts to different stain types rather than requiring completely separate systems for each stain, reducing processing complexity while maintaining the ability to accurately identify various tissue types.
Data Source
AI summary
One or more example embodiments are methods and corresponding systems for providing a training data set for training a segmentation algorithm for segmenting whole-slide images in digital pathology as well as the use of the training data and corresponding ML segmentation algorithms. For example, a first segmentation of a whole slide image is refined based on an automatically generated annotation which has a higher level of detail than the first segmentation. A second segmentation results, which may be used as a ground truth for training the ML segmentation algorithm on the basis of the whole slide image.