Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells


Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.

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  1. 1.

    Lee SH, Erber WN, Porwit A, Tomonaga M, Peterson LC. International Council for Standardization In H. ICSH guidelines for the standardization of bone marrow specimens and reports. Int J Lab Hematol. 2008;30:349–64.

  2. 2.

    Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, et al. WHO classification of tumours of haematopoietic and lymphoid tissues. Vol 2. 4th ed. Lyon, France: IARC publications; 2017. p. 585.

  3. 3.

    Abdulrahman AA, Patel KH, Yang T, Koch DD, Sivers SM, Smith GH, et al. Is a 500-cell count necessary for bone marrow differentials? A proposed analytical method for validating a lower cutoff. Am J Clin Pathol. 2018;150:84–91.

  4. 4.

    d’Onofrio G, Zini G. Analysis of bone marrow aspiration fluid using automated blood cell counters. Clin Lab Med. 2015;35:25–42.

  5. 5.

    Mori Y, Mizukami T, Hamaguchi Y, Tsuruda K, Yamada Y, Kamihira S. Automation of bone marrow aspirate examination using the XE-2100 automated hematology analyzer. Cytometry B Clin Cytom. 2004;58:25–31.

  6. 6.

    Kratz A, Bengtsson HI, Casey JE, Keefe JM, Beatrice GH, Grzybek DY, et al. Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network. Am J Clin Pathol. 2005;124:770–81.

  7. 7.

    Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform. 2016;7:29.

  8. 8.

    Sirinukunwattana K, Ahmed Raza SE, Yee-Wah T, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35:1196–206.

  9. 9.

    Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velazquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci USA. 2018;115:E2970–9.

  10. 10.

    Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 2018;23:181–93 e7.

  11. 11.

    Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8:3395.

  12. 12.

    Senaras C, Niazi MKK, Lozanski G, Gurcan MN. DeepFocus: detection of out-of-focus regions in whole slide digital images using deep learning. PLoS ONE. 2018;13:e0205387.

  13. 13.

    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

  14. 14.

    Choi JW, Ku Y, Yoo BW, Kim JA, Lee DS, Chai YJ, et al. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PloS ONE. 2017;12:e0189259.

  15. 15.

    Reta C, Altamirano L, Gonzalez JA, Diaz-Hernandez R, Peregrina H, Olmos I, et al. Segmentation and classification of bone marrow cells images using contextual information for medical diagnosis of acute leukemias. PLoS ONE. 2015;10:e0130805.

  16. 16.

    Gutman DA, Khalilia M, Lee S, Nalisnik M, Mullen Z, Beezley J, et al. The digital slide archive: a software platform for management, integration, and analysis of histology for cancer research. Cancer Res. 2017;77:e75–8.

  17. 17.

    Glassy EF. Color atlas of hematology; an illustrated field guide based on proficiency testing. Illinois, USA: College of American Pathologists; 1998.

  18. 18.

    Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39:1137–49.

  19. 19.

    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society; 2016. pp. 770–8.

  20. 20.

    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014.

  21. 21.

    Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning. New York, NY: ACM; 2006. pp. 233–40.

  22. 22.

    Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009;45:427–37.

  23. 23.

    Bain BJ, Bates I, Laffan M, Lewis SM. Dacie and Lewis practical hematology. 11th ed. London: Churchill Livingstone; 2011. p. 668.

  24. 24.

    Ryan DH. Examination of the marrow. In: Kaushansky K, Lichtman MA, Prchal JT, editors. Williams hematology. 9th ed. New York, NY: McGraw-Hill Education; 2015. p 27–40.

  25. 25.

    Vollmer RT. Blast counts in bone marrow aspirate smears: analysis using the poisson probability function, bayes theorem, and information theory. Am J Clin Pathol. 2009;131:183–8.

  26. 26.

    Cornet E, Perol JP, Troussard X. Performance evaluation and relevance of the CellaVision DM96 system in routine analysis and in patients with malignant hematological diseases. Int J Lab Hematol. 2008;30:536–42.

  27. 27.

    Briggs C, Longair I, Slavik M, Thwaite K, Mills R, Thavaraja V, et al. Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system. Int J Lab Hematol. 2009;31:48–60.

  28. 28.

    Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25:1301–9.

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This research was supported by the National Cancer Institute Informatics Technology for Cancer Research grants U01CA220401 and U24CA19436201. We thank Drs Geoffrey Smith and Thomas Durant for helpful suggestions and comments on the paper. RC and NK performed this work as part of a senior research course at the Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka (instructor Dr. N. W. Nuwan Dayananda, PhD). We thank the University of Moratuwa and Dr Dayanada for their support.


This research was supported in part by the National Cancer Institute Informatics Technology for Cancer Research grants U01CA220401 and U24CA19436201.

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AAA and BRD generated and reviewed annotations. RC and NK developed algorithms and performed experiments. MA and DAG provided technical support for the annotation platform and database. LADC directed development and implementation of the annotation protocol, all computational approaches, and designed experiments. DLJ reviewed annotations, provided slides, conceived of the problem, and directed the project. RC, AAA, LADC, and DLJ wrote and edited the paper.

Correspondence to Lee A. D. Cooper or David L. Jaye.

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This work was presented at the Academy of Clinical Laboratory Physicians and Scientists (ACLPS) 2018 meeting as a platform presentation.

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Chandradevan, R., Aljudi, A.A., Drumheller, B.R. et al. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Invest 100, 98–109 (2020) doi:10.1038/s41374-019-0325-7

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