The data is collected from the subjects suffering from Multiple Myeloma (MM), who came with the symptoms of cancer for diagnosis and/or who are under treatment at the AIIMS, New Delhi, India. Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with MM. MM is a type of white blood cancer, where the plasma cells of blood are involved. Slides were stained using Jenner-Giemsa stain and plasma cells are required to be segmented. Images were captured in raw BMP format using two cameras with:
- size of 2040x1536 pixels using cellSens software Version 2.1 (Olympus) attached to the microscope
- size of 1920x2560 pixels from a Nikon camera attached to the microscope.
A total of 775 images are stain color normalized using our in-house methodology. These are divided into the 1) training set of 298 images, 2) Validation set of 200 images, and the test set of 277 images. The dataset was used in the IEEE ISBI 2021 medical image challenge dataset. The leaderboard of the challenge is active. The ground truth of the training and validation dataset are provided, while the GT of the test set will not be shared. The researchers can check the performance on the test dataset by uploading results at the leaderboard at https://segpc-2021.grand-challenge.org/evaluation/final-test-phase/leaderboard/
Access Data Here
The dataset is available at the IEEE Dataport repository. Please visit this link to download the dataset.
Citations & Data Usage Policy
Please acknowledge both this dataset and related publications by including the following citations in your work::
Anubha Gupta, Ritu Gupta, Shiv Gehlot, and Shubham Goswami, "SegPC-2021: Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images", IEEE Dataport , DOI: https://dx.doi.org/10.21227/7np1-2q42.
- Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788. (2020 IF: 11.148)
- Shiv Gehlot, Anubha Gupta and Ritu Gupta, "EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Barcelona, Spain, 2020, pp. 1389-1393.
- Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma," PLoS ONE 13(12): e0207908, Dec 2018. DOI: 10.1371/journal.pone.0207908 .