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Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with B-lineage acute lymphoid leukemia and multiple myeloma as per the standard guidelines. Slides were stained using Jenner-Giemsa stain. Images were captured at 1000x magnication using Nikon Eclipse-200 microscope equipped with a digital camera. Images were captured in raw BMP format with a size of 2560x1920 pixels. In all, this dataset consists of 30 images of B-ALL and 30 images of MM. Both MM and B-ALL images have sucient variability from one image to another image to rigorously test any stain normalization methodology developed.

Data Access

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Data TypeDownload all or Query/Filter
Images (.bmp, TIFF, 900MB)


Supplemental Data (txt)

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Detailed Description

Image Statistics



Number of Patients


Number of Studies


Number of Images


Images Size (MB)900

Citations & Data Usage Policy

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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 Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

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Publication Citation

[1] Anubha Gupta, Rahul Duggal, Ritu Gupta, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, “GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images”, under review.
[2] Ritu Gupta, Pramit Mallick, Rahul Duggal, Anubha Gupta, and Ojaswa Sharma, "Stain Color Normalization and Segmentation of Plasma Cells in Microscopic Images as a Prelude to Development of Computer Assisted Automated Disease Diagnostic Tool in Multiple Myeloma," 16th International Myeloma Workshop (IMW), India, March 2017.

[3] Rahul Duggal, Anubha Gupta, Ritu Gupta, and Pramit Mallick, "SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging," In: Descoteaux M., Maier- Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, MICCAI 2017. Lecture Notes in Computer Science, Part III, LNCS 10435, pp. 435–443. Springer, Cham. DOI: 3-319-66179-7_50.
[4] Mourya S., Kant, S., Kumar, P., Gupta, A. and Gupta, R., 2018. LeukoNet: DCT-based CNN architecture for the classication of normal versus Leukemic blasts in B-ALL Cancer. arXiv preprint arXiv:1810.07961.

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

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Version 1 (Current): Updated 2019/03/26

Data TypeDownload all or Query/Filter
Images (.bmp, TIFF, 900MB)

(Requires NBIA Data Retriever.)

Other (txt)

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