Summary

The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Grünwald-Giemsa/Pappenheim stain. The diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. Image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion.

Large datasets with a high quality of both data acquisition and annotation are key prerequisites to develop data-driven, computational methods in diagnostic medicine. In the case of bone marrow morphology, a key diagnostic method for a broad range of hematologic diseases, only few datasets are publicly available so far, which are orders of magnitude smaller than the one presented here. Inclusion of our dataset into TCIA provides both medical researchers and bioinformaticians with a public resource for education and algorithm improvement.

All samples were processed in the Munich Leukemia Laboratory (MLL), scanned using equipment developed at Fraunhofer IIS and post-processed using software developed at Helmholtz Munich.

Acknowledgements


Data Access

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Detailed Description

Image Statistics

Pathology Image Statistics

Modalities

Pathology

Number of Patients

945

Number of Images

171,375

Images Size (GB)6.8


Abbreviations:

ABEAbnormal eosinophil
ARTArtefact
BASBasophil
BLABlast
EBOErythroblast
EOSEosinophil
FGCFaggott cell
HACHairy cell
KSCSmudge cell
LYIImmature lymphocyte
LYTLymphocyte
MMZMetamyelocyte
MONMonocyte
MYBMyelocyte
NGBBand neutrophil
NGSSegmented neutrophil
NIFNot identifiable
OTHOther cell
PEBProerythroblast
PLMPlasma cell
PMOPromyelocyte



Citations & Data Usage Policy

Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021). An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.AXH3-T579


Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., and Marr, C. (2021). Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image dataset. https://doi.org/10.1182/blood.2020010568


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

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Version 1 (Current): Updated 2021/11/12

Data TypeDownload all or Query/Filter
Tissue Slide Images (JPG, 6.8GB)