An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears

Joshua E. Lewis, Conrad W. Shebelut, Bradley R. Drumheller, Xuebao Zhang, Nithya Shanmugam, Michel Attieh, Michael C. Horwath, Anurag Khanna, Geoffrey H. Smith, David A. Gutman, Ahmed Aljudi, Lee A.D. Cooper, David L. Jaye

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

Original languageEnglish (US)
Pages (from-to)100003
Number of pages1
JournalModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Issue number2
StatePublished - Feb 1 2023


  • aspirate
  • bone marrow
  • differential
  • machine learning
  • whole-slide imaging

ASJC Scopus subject areas

  • Pathology and Forensic Medicine


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