Select Language

公开数据集

WBC Image Dataset 1

WBC Image Dataset 1

Scene:

Medical

Data Type:

2D Semantic Segmentation
所需积分:10 去赚积分?
  • 177浏览
  • 0下载
  • 0点赞
  • 收藏
  • 分享

贡献者查看主页

小小程序员

致力于人工智能业务的研究、数据集处理。

Data Preview ? 6.66M

    Data Structure ?

    *数据结构实际以真实数据为准

    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}
    }


    0相关评论
    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。