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Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks

Nature Machine Intelligence volume 1, pages 538–544 (2019)Cite this article

Subjects

A preprint version of the article is available at bioRxiv.

Abstract

Reliable recognition of malignant white blood cells is a key step in the diagnosis of haematologic malignancies such as acute myeloid leukaemia. Microscopic morphological examination of blood cells is usually performed by trained human examiners, making the process tedious, time-consuming and hard to standardize. Here, we compile an annotated image dataset of over 18,000 white blood cells, use it to train a convolutional neural network for leukocyte classification and evaluate the network’s performance by comparing to inter- and intra-expert variability. The network classifies the most important cell types with high accuracy. It also allows us to decide two clinically relevant questions with human-level performance: (1) if a given cell has blast character and (2) if it belongs to the cell types normally present in non-pathological blood smears. Our approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.

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Fig. 1: Data handling workflow.
Fig. 2: Classification of 18,000 single-cell images into an 18-class scheme.
Fig. 3: Human-level network performance in single-cell classification, pixel-wise attention and binary decision tasks.

Data availability

The full single-cell image dataset and corresponding annotations are publicly available at The Cancer Imaging Archive (TCIA): https://doi.org/10.7937/tcia.2019.36f5o9ld 43.

Code availability

Code for the network trained in this study and network weights for one fold are available on CodeOcean, together with a subset of the single-cell image data used to test the network: https://codeocean.com/capsule/9068249/tree/v1 44.

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Acknowledgements

We thank N. Chlis for comments on the manuscript, K. Metzeler for helpful discussions and A. Holzäpfel for contributions to the annotation task. This work was supported by the German Research Foundation DFG within the Collaborative Research Center SFB 1243. C. Matek acknowledges support from Deutsche José Carreras-Leukämie Stiftung.

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Author notes

  1. Joint corresponding authors: Karsten Spiekermann, Carsten Marr.

  2. These authors jointly supervised this work: Karsten Spiekermann, Carsten Marr.

Authors and Affiliations

  1. Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Christian Matek & Carsten Marr

  2. Laboratory of Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany

    Christian Matek, Simone Schwarz & Karsten Spiekermann

  3. German Cancer Consortium (DKTK), Heidelberg, Germany

    Karsten Spiekermann

  4. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Karsten Spiekermann

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  1. Christian Matek
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Contributions

C. Matek, C. Marr and K.S. conceived the initial idea. C. Matek selected the cohort, digitized blood smears, wrote annotation software, and trained and evaluated the network. S.S. contributed to selecting the cohort and annotated the image data. C. Matek, C. Marr and K.S. interpreted data and wrote the paper. All authors approved the manuscript.

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Correspondence to Karsten Spiekermann or Carsten Marr.

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Matek, C., Schwarz, S., Spiekermann, K. et al. Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat Mach Intell 1, 538–544 (2019). https://doi.org/10.1038/s42256-019-0101-9

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