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DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images
Nicholas Lucarelli1, Seth Winfree2, Angela Sabo3
1Department of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL, USA.
Insights
DigitAb, a deep learning tool, identifies kidney cell types from standard H&E stains, eliminating costly immunostaining. This accessible technology aids disease diagnosis and research in kidney transplant rejection and diabetic nephropathy.
Area of Science:
- Computational pathology
- Digital pathology
- Artificial intelligence in medicine
Background:
- Histological stains like H&E are crucial for disease diagnosis and research.
- Immunostaining enhances cellular detail but is costly and complex.
- Multiplex imaging offers broad cellular coverage but faces accessibility challenges.
Purpose of the Study:
- To develop an accessible deep learning framework (DigitAb) for cell type classification directly from H&E stained kidney biopsy slides.
- To eliminate the need for specialized immunostaining or multiplex imaging assays in routine diagnostics.
- To enable scalable and cost-effective cellular segmentation for research and clinical pathology.
Main Methods:
- Trained a semantic segmentation model using DigitAb on ~3.5 million cells from 29 human kidney samples with Phenocycler-generated ground truths.
- Utilized adversarial domain adaptation to test DigitAb on unlabeled biopsy samples.
- Validated cell type predictions against the Banff schema for kidney transplant rejection and diabetic nephropathy characteristics.
Main Results:
- Achieved a balanced accuracy of 0.78 in classifying 10 cell types from H&E slides.
- DigitAb successfully predicted cell types from unlabeled kidney biopsy samples.
- Demonstrated high concordance with clinical gold standards for kidney transplant rejection and diabetic nephropathy.
Conclusions:
- DigitAb provides a scalable, accessible, and label-free solution for cellular segmentation using standard histology.
- The framework significantly reduces reliance on specialized assays, making advanced cellular analysis more widely available.
- This deep learning approach holds promise for improving kidney disease diagnosis and research.
Abstract:
Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated high-resolution ground truths for ~3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloud based tool, DigitAb, provides scalable, accessible, label free cellular segmentation for research and clinical pathology.
