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Summary


The Munich AML Morphology Dataset contains 18,365 expert-labeled single-cell images taken from peripheral blood smears of 100 patients diagnosed with Acute Myeloid Leukemia at Munich University Hospital between 2014 and 2017, as well as 100 patients without signs of hematological malignancy. Image acquisition was done using a M8 digital microscope / scanner (Precipoint GmbH, Freising, Germany) at 100-fold optical magnification and oil immersion. Pathological and non-pathological leukocytes were classified into a standard morphological classification scheme derived from clinical practice by trained experts. To quantify inter- and intra-rater variability of examiners, a subset of images was re-annotated up to two times. The dataset has been used by the authors to train a convolutional neural network for single-cell morphology classification.


Acknowledgements

We would like to acknowledge the following individuals and institutions:

  • The authors wish to thank Antje Holzäpfel for contributions to the annotation task.
  • German Research Foundation, grant SFB 1243.
  • Christian Matek gratefully acknowledges support from Deutsche José Carreras-Leukämie Stiftung.

Data Access

Data TypeDownload all
Images (TIFF, 11 GB)

Abbreviations (TXT)

Annotations (DAT)

Detailed Description

Image Statistics


Modalities

Pathology

Number of Patients

200

Number of Images

18,365

Images Size (GB)11

Additional Information about Data

  • All single-cell images in this dataset were produced using the M8 digital microscope/scanner (Precipoint GmbH, Freising/Germany) from peripheral blood smears at 100x magnification and oil immersion. A coverage of 14.14 Pixels per Micron is given by the manufacturer.
  • The abbreviations used for morphological classes in annotations and the folder structure are defined in abbreviations.txt
  • Annotations are given in the file annotations.dat. In this file, the first column gives the name of the respective image file and the second column the morphological class assigned during the gold-standard annotation. If a single-cell image was re-annotated, the result of the first re-annotation process by a second independent annotator is given in the third column, and the result of the second re-annotation process after a time interval of 11 months by the same re-annotator in the fourth column. If a single-cell image was not re-annotated, the third and fourth column contain the value "nan".
  • For details of the scanning and annotation process, please refer to:
    • Matek et al., Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks., Nat. Mach. Intell. (2019)


Citations & Data Usage Policy

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

"Matek, C., Schwarz, S., Marr, C., & Spiekermann, K. (2019). A Single-cell Morphological Dataset of Leukocytes from AML Patients and Non-malignant Controls [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.36f5o9ld"

Publication Citation

1) Matek et al., Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks, Nature Machine Intelligence (2019, accepted)

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 1 (Current): Updated 2019/10/24

Data TypeDownload all
Images (TIFF, 11 GB)

Abbreviations (TXT)

Annotations (DAT)





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