Multiplex Cell Imaging Classification With Deep Learning Marker Quantification
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Solution Overview
Problem
Current methods for analyzing multiplex immunofluorescence images are laborious, user-dependent, and underperforming, especially for rare cell types, lacking a robust and automated pipeline for multiplex imaging analysis.
Innovation Solution
A deep learning pipeline comprising a multi-classifier for classifying multi-channel cell images into different cell types and a binary classifier for determining marker positivity, trained on labeled tiles from a publicly available dataset, enabling accurate cell typing and phenotypic marker quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clustering and manual thresholding methods are used for cell typing, then the analysis can be performed with simple tools, but the process becomes laborious and user-dependent with poor performance especially for rare cell types
Solution Approach 1:
The patent replaces manual thresholding and clustering operations with a deep learning-based automated pipeline. The system uses trained neural networks to automatically classify cells into types based on multiplex imaging data, eliminating the need for user-dependent manual adjustments and iterative thresholding while significantly improving accuracy for both common and rare cell types.
Solution Approach 2:
The deep learning model performs self-service by automatically learning optimal classification thresholds and decision boundaries during training. The system independently identifies cell types without requiring user intervention for parameter tuning, making the process both automated and adaptive to different datasets.
2Productivity
If automated machine learning algorithms are used for cell typing, then productivity increases, but measurement precision decreases with F1-scores between 0.6 and 0.7 for common cell types and 0.4 to 0.6 for rare cell types
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on large datasets of multiplex imaging data before deployment. The models learn robust cell type classifications during the training phase, enabling them to achieve high accuracy (F1-scores above 0.8) when processing new data, thus resolving the precision-loss issue of previous automated methods.
Solution Approach 2:
The system changes parameters by using deep learning architectures with multiple layers and non-linear transformations, unlike simpler automated algorithms. This allows the model to capture complex patterns in the data, improving classification accuracy for both common and rare cell types while maintaining high productivity.
3Ease of manufacture
If ground truth based on clustering-based cell typing is used for evaluation, then the evaluation process is simple, but the validity of the metrics is not established
Solution Approach 1:
The patent inverts the traditional evaluation approach by using experimentally validated cell type annotations as ground truth rather than clustering results. Pathologists or domain experts provide verified labels for training and evaluation datasets, ensuring that the ground truth is biologically valid and reliable, while the system automatically computes performance metrics.
Data Source
AI summary
A system and method of classifying cells may include receiving a multichannel image depicting biological cells in a pathology slide, wherein said multichannel image comprises a plurality of channels, corresponding to a respective plurality of protein marker types; extracting, from the multichannel image, one or more multichannel tiles, each depicting a predetermined area that surrounds a center point of a specific, respective cell; splitting at least one of the one or more multichannel tiles into a plurality of single-channel tiles, corresponding to said plurality of protein marker types; inferring a pretrained, single-channel Machine Learning (ML) based classifier on one or more of the single-channel tiles, to predict one or more respective protein marker expression probability values; and identifying a type of the specific cell based on the one or more protein marker expression probability values.