Duplex Array Ground-Truth Generation With Interactive Cell Reclassification
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Solution Overview
Problem
Current methods for generating ground-truth data for digital pathology images are labor-intensive, time-consuming, and resource-intensive, particularly for multiplex assays like duplex immunohistochemistry, due to the difficulty in collecting and labeling diverse and privacy-constrained medical images, and existing deep-learning models struggle to generalize across different image domains and user inputs.
Innovation Solution
A computer-implemented method using a deep learning model for interactive segmentation of digital pathology images, combined with a graphical user interface, allows users to efficiently update cell classifications and region labels, enabling rapid and accurate segmentation and classification of cells in multiplex assays, even across different domains, by leveraging multi-class training data and click annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and classification by pathologists is used, then accuracy of cell classification is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
A deep learning model serves as an intermediary between the digital pathology image and the final classification result. The model is trained on multi-class training data with click annotations to automatically perform segmentation and classification, acting as a mediator that reduces the need for manual pathologist review while maintaining high accuracy through learned patterns from diverse training data.
Solution Approach 2:
The system performs preliminary segmentation and classification using the trained deep learning model before any manual review. This preliminary action processes the entire image automatically, identifying candidate regions and classifications that can then be quickly verified or corrected by pathologists, rather than requiring manual analysis of every cell from scratch.
2Reliability
If deep learning models are trained with large datasets, then model accuracy and generalization improve, but data collection and labeling become more difficult due to privacy constraints
Solution Approach 1:
The deep learning model is designed with multi-functionality to handle multiple types of digital pathology images including H&E stains, immunohistochemistry (IHC) images, and multiplex assays. By training on diverse multi-class training data covering different image domains and staining types, the model achieves universal applicability across various medical imaging tasks, reducing the need for separate models for each specific assay type.
Solution Approach 2:
The system employs parameter changes in the form of data augmentation and normalization techniques to adapt training data across different domains. By transforming and normalizing diverse input images with different staining protocols and scanner types, the model learns domain-invariant features that improve generalization without requiring equally diverse training data for each specific domain.
3Ease of operation
If random sampling is used to select training images, then data selection is simple, but training efficiency decreases as non-informative samples waste resources
Solution Approach 1:
The system implements feedback mechanisms where the deep learning model's performance on validation data informs the selection of additional training samples. Samples that are most informative for improving model performance (e.g., edge cases, uncertain predictions) are selected for labeling and addition to the training set, creating a feedback loop that continuously improves training efficiency rather than using random sampling.
4Extent of automation
If deep learning models are applied to digital pathology images, then automation increases, but adaptability to different image domains and stains decreases
Solution Approach 1:
The deep learning model is designed with universal architecture and training protocols that enable it to handle multiple image domains including H&E stains, immunohistochemistry (IHC) images, and multiplex assays. The model processes digital pathology images through standardized preprocessing and normalization, achieving high automation while maintaining adaptability across different staining types and scanner platforms through domain-invariant feature learning.
Data Source
AI summary
Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.