BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting

Zhongyi Huang; Yao Ding; Guoli Song; Lin Wang; Ruizhe Geng; Hongliang He; Shan Du; Xia Liu; Yonghong Tian; Yongsheng Liang; S. Kevin Zhou; Jie Chen

doi:10.1007/978-3-030-59722-1_28

BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting

Zhongyi Huang
, Yao Ding
, Guoli Song
, Lin Wang
, Ruizhe Geng
, Hongliang He
, Shan Du
, Xia Liu
, Yonghong Tian
, Yongsheng Liang
, S. Kevin Zhou
, Jie Chen^*

^*此作品的通讯作者

Peking University
University of Chinese Academy of Sciences
Peng Cheng Laboratory
Shenzhen University
Harbin Institute of Technology Shenzhen
CAS - Institute of Computing Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

36 引用（Scopus）

摘要

Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://sites.google.com/view/bcdataset

源语言	英语
主期刊名	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
编辑	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
出版商	Springer Science and Business Media Deutschland GmbH
页	289-298
页数	10
ISBN（印刷版）	9783030597214
DOI	https://doi.org/10.1007/978-3-030-59722-1_28
出版状态	已出版 - 2020
已对外发布	是
活动	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, 秘鲁期限: 4 10月 2020 → 8 10月 2020

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	12265 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
国家/地区	秘鲁
市	Lima
时期	4/10/20 → 8/10/20

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1007/978-3-030-59722-1_28

其它文件与链接

链接到 Scopus 的出版物

引用此

Huang, Z., Ding, Y., Song, G., Wang, L., Geng, R., He, H., Du, S., Liu, X., Tian, Y., Liang, Y., Zhou, S. K., & Chen, J. (2020). BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting. 在 A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (编辑), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (页码 289-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12265 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59722-1_28

Huang, Zhongyi ; Ding, Yao ; Song, Guoli 等. / BCData : A Large-Scale Dataset and Benchmark for Cell Detection and Counting. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. 编辑 / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. 页码 289-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ca5eb41d52a840018e7d498430384f81,

title = "BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting",

abstract = "Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://sites.google.com/view/bcdataset",

keywords = "Breast tumor cell dataset, Cell counting, Cell detection",

author = "Zhongyi Huang and Yao Ding and Guoli Song and Lin Wang and Ruizhe Geng and Hongliang He and Shan Du and Xia Liu and Yonghong Tian and Yongsheng Liang and Zhou, \{S. Kevin\} and Jie Chen",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-59722-1\_28",

language = "English",

isbn = "9783030597214",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "289--298",

editor = "Martel, \{Anne L.\} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, \{Maria A.\} and Zhou, \{S. Kevin\} and Daniel Racoceanu and Leo Joskowicz",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",

address = "Germany",

}

Huang, Z, Ding, Y, Song, G, Wang, L, Geng, R, He, H, Du, S, Liu, X, Tian, Y, Liang, Y, Zhou, SK & Chen, J 2020, BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting. 在 AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (编辑), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 12265 LNCS, Springer Science and Business Media Deutschland GmbH, 页码 289-298, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, 秘鲁, 4/10/20. https://doi.org/10.1007/978-3-030-59722-1_28

BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting. / Huang, Zhongyi; Ding, Yao; Song, Guoli 等.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. 编辑 / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. 页码 289-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12265 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - BCData

T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

AU - Huang, Zhongyi

AU - Ding, Yao

AU - Song, Guoli

AU - Wang, Lin

AU - Geng, Ruizhe

AU - He, Hongliang

AU - Du, Shan

AU - Liu, Xia

AU - Tian, Yonghong

AU - Liang, Yongsheng

AU - Zhou, S. Kevin

AU - Chen, Jie

PY - 2020

Y1 - 2020

N2 - Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://sites.google.com/view/bcdataset

AB - Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://sites.google.com/view/bcdataset

KW - Breast tumor cell dataset

KW - Cell counting

KW - Cell detection

UR - https://www.scopus.com/pages/publications/85092742286

U2 - 10.1007/978-3-030-59722-1_28

DO - 10.1007/978-3-030-59722-1_28

M3 - Conference contribution

AN - SCOPUS:85092742286

SN - 9783030597214

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 289

EP - 298

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 4 October 2020 through 8 October 2020

ER -

Huang Z, Ding Y, Song G, Wang L, Geng R, He H 等. BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting. 在 Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, 编辑, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. 页码 289-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59722-1_28

BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此