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MIMM_SBILAB

The Cancer Imaging Archive

MiMM_SBILab | MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma

DOI: 10.7937/tcia.2019.pnn6aypl | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Bone Human 5 Histopathology Multiple Myeloma 1.28GB Image Analyses Public, Complete 2019/03/25

Summary

Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with multiple myeloma as per the standard guidelines. Slides were stained using Jenner- Giemsa stain. Images were captured at 1000x magnification 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 85 images. All these 85 images were stain normalized using our in-house methodology before being used for segmentation. These stain normalized images have been provided as the annotated dataset with plasma cells marked in all image slides contained in a presentation for the ready reference of readers.

Additional Notes

This collection has also been uploaded to the Harvard Blood Cancer Dataverse website. Please refer to DOI 10.7910/DVN/XCX7ST for more information.

Data Access

Version 1: Updated

Title Data Type Format Access Points Subjects Studies Series Images License
Slide Images Histopathology BMP
Download requires IBM-Aspera-Connect plugin
5 5 85 CC BY 3.0
Annotated plasma cell images PDF CC BY 3.0

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Gupta, R., & Gupta, A. (2019). MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.pnn6aypl

Other Publications Using this Data

The following publications are recommended by the data submitters that may be useful to researchers utilizing this collection:

  • 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 https://doi.org/10.1016/j.clml.2017.03.178

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

Publication Citation

Gupta, A., Duggal, R., Gehlot, S., Gupta, R., Mangal, A., Kumar, L., Thakkar, N., & Satpathy, D. (2020). GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. Medical Image Analysis, 65, 101788. https://doi.org/10.1016/j.media.2020.101788

Publication Citation

Gupta, A., Mallick, P., Sharma, O., Gupta, R., & Duggal, R. (2018). PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma. PLOS ONE, 13(12), e0207908. https://doi.org/10.1371/journal.pone.0207908

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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7