Immunohistochemistry-assisted deep learning for lineage identification in rat bone marrow from hematoxylin and eosin whole-slide images.

The analysis of hematoxylin and eosin (HE)-stained bone marrow (BM) tissue sections in drug development toxicologic pathology studies is a key step in the in vivo safety assessment of new drug candidates. Routine histologic analysis provides critical information about the cellularity and tissue architecture of the BM but only limited insights into the cell lineages that comprise the hematopoietic tissue. The evaluation of BM cell types can be augmented by examining BM smear preparations or using immunohistochemical (IHC) labeling of histologic sections to identify lineages of interest; however, neither of these approaches is included in the standard assessment. In addition, manual evaluation is time-consuming, subject to inter-observer variability, and challenging due to the complexity of BM morphology and architecture. In this project, we developed a deep learning model to predict IHC labeling of BM cell lineages on HE-stained BM tissue sections. The model is trained on an immunohistochemistry-informed, HE-based ground truth for the sequential labeling of CD11b and myeloperoxidase markers and can predict cell segmentation mappings with over 69% agreement with the ground truth, using only HE slides as input. Furthermore, our method can discern cell population changes that reflect qualitative diagnoses identified by pathologists during routine slide interpretation. Our automated method holds promise for enhancing routine pathologist assessments of BM HE slides by extending the evaluation of hematopoietic cell lineages without the need to generate additional samples.