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Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning
Shima Mehrvar1, Kevin Maisonave1, Wayne Buck1
1AbbVie Inc., North Chicago, Illinois, USA.
Insights
A deep learning model quantifies spleen compartments from H&E slides, improving immune system histopathology. This AI tool enhances accuracy and efficiency in toxicologic assessments of lymphoid tissues.
Area of Science:
- Immunotoxicology
- Computational Pathology
Background:
- Assessing lymphocyte populations in lymphoid organs during toxicology studies is challenging due to sampling variability and limited cytologic detail in H&E staining.
- While immunohistochemistry provides definitive T- and B-cell characterization, routine toxicologic evaluations rely solely on H&E slides.
Purpose of the Study:
- To develop and validate a deep learning model for precise, compartment-specific quantification of splenic lymphoid tissues using H&E stained slides.
- To establish a quantitative baseline for normal splenic lymphoid compartment area and cellularity.
Main Methods:
- A deep learning model was trained on H&E stained rat spleen slides, using co-registered images from destained and dual-labeled (CD3, CD79a) slides as ground truth.
- The model was validated for its accuracy in identifying splenic compartments: periarteriolar lymphoid sheaths, follicles, germinal centers, and marginal zones.
Main Results:
- The deep learning model achieved high accuracy (97.8% Dice similarity coefficient) in quantifying splenic compartments directly from H&E stained slides.
- The model was successfully applied to determine the normal range of splenic lymphoid compartment area and cellularity.
Conclusions:
- Deep learning models can accurately quantify splenic lymphoid compartments from routine H&E slides, overcoming limitations of traditional histopathology.
- This approach offers potential for improved accuracy, precision, and time efficiency in enhanced immune system histopathology evaluations.
- Expansion to other lymphoid tissues and integration into routine toxicologic pathology workflows is recommended.
Abstract:
Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and limited distinctive cytologic features of lymphocyte subpopulations as seen with hematoxylin and eosin (H&E) staining. Although immunohistochemistry is necessary for definitive characterization of T- and B-cell compartments, routine toxicologic assessments are based solely on H&E slides. Here, a deep learning (DL) model was developed using normal rats to quantify relevant compartments of the spleen, including periarteriolar lymphoid sheaths, follicles, germinal centers, and marginal zones from H&E slides. Slides were scanned, destained, dual labeled with CD3 and CD79a chromogenic immunohistochemistry, and rescanned to generate exact co-registered images that served as the ground truth for training and validation. The DL model identified individual splenic compartments with high accuracy (97.8% Dice similarity coefficient) directly from H&E-stained tissue. The DL model was utilized to study the normal range of lymphoid compartment area and cellularity. Future implementation of our DL model and expanding this approach to other lymphoid tissues have the potential to improve accuracy and precision in enhanced histopathology evaluation of the immune system with concurrent gains in time efficiency for the pathologist.
