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WBC Image Dataset 1
2D Semantic Segmentation
Medical
|...
License: GPL-3.0

Overview

This is two datasets of white blood cell (WBC) images used for “Fast and Robust Segmentation
of White Blood Cell Images by Self-supervised Learning
”, which can be used to evaluate cell
image segmentation methods.

These two datasets are significantly different from each other
in terms of the image color, cell shape, background, etc., which can better evaluate the robustness
of WBC segmentation approach. The ground truth segmentation results are manually sketched by
domain experts, where the nuclei, cytoplasms and background including red blood cells are marked
in white, gray and black respectively. We also submitted the segmentation results by our approach,
where the whole WBC region are marked in white and the others are marked in black.

Dataset
1
was obtained from Jiangxi Tecom Science Corporation, China.
It contains three hundred 120×120 images of WBCs and their color depth is 24 bits. The images
were taken by a Motic Moticam Pro 252A optical microscope camera with a N800-D motorized auto-focus
microscope, and the blood smears were processed with a newly-developed hematology reagent for
rapid WBC staining. The overall background of most of the images of Dataset 1 looks yellow.

Images from Dataset 1.

Instruction

The class labels of each image in Dataset 1 is shown in the files Class Labels of Dataset
1.csv
. The labels (1- 5) represent neutrophil, lymphocyte, monocyte, eosinophil and basophil,
respectively.

Citation

Please use the following citation when referencing the dataset:

@article{Zheng2018,
  title={Fast and Robust Segmentation of White Blood Cell Images by Self-supervised Learning},
  author={Xin Zheng and Yong Wang and Guoyou Wang and Jianguo Liu},
  journal={Micron},
  volume={107},
  pages={55--71},
  year={2018},
  publisher={Elsevier}
  doi={https://doi.org/10.1016/j.micron.2018.01.010},
  url={https://www.sciencedirect.com/science/article/pii/S0968432817303037}
}

License

GPL-3.0

Data Summary
Type
Image,
Amount
300
Size
6.66MB
Provided by
Tecom Science Corporation
Founded in 1992, Tecom Science Corporation is a national high-tech enterprise specialized in developing, manufacturing and selling high-end medical equipment and IVD reagents.
| Amount 300 | Size 6.66MB
WBC Image Dataset 1
2D Semantic Segmentation
Medical
License: GPL-3.0

Overview

This is two datasets of white blood cell (WBC) images used for “Fast and Robust Segmentation
of White Blood Cell Images by Self-supervised Learning
”, which can be used to evaluate cell
image segmentation methods.

These two datasets are significantly different from each other
in terms of the image color, cell shape, background, etc., which can better evaluate the robustness
of WBC segmentation approach. The ground truth segmentation results are manually sketched by
domain experts, where the nuclei, cytoplasms and background including red blood cells are marked
in white, gray and black respectively. We also submitted the segmentation results by our approach,
where the whole WBC region are marked in white and the others are marked in black.

Dataset
1
was obtained from Jiangxi Tecom Science Corporation, China.
It contains three hundred 120×120 images of WBCs and their color depth is 24 bits. The images
were taken by a Motic Moticam Pro 252A optical microscope camera with a N800-D motorized auto-focus
microscope, and the blood smears were processed with a newly-developed hematology reagent for
rapid WBC staining. The overall background of most of the images of Dataset 1 looks yellow.

Images from Dataset 1.

Instruction

The class labels of each image in Dataset 1 is shown in the files Class Labels of Dataset
1.csv
. The labels (1- 5) represent neutrophil, lymphocyte, monocyte, eosinophil and basophil,
respectively.

Citation

Please use the following citation when referencing the dataset:

@article{Zheng2018,
  title={Fast and Robust Segmentation of White Blood Cell Images by Self-supervised Learning},
  author={Xin Zheng and Yong Wang and Guoyou Wang and Jianguo Liu},
  journal={Micron},
  volume={107},
  pages={55--71},
  year={2018},
  publisher={Elsevier}
  doi={https://doi.org/10.1016/j.micron.2018.01.010},
  url={https://www.sciencedirect.com/science/article/pii/S0968432817303037}
}

License

GPL-3.0

0
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