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*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

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

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages289-298
Number of pages10
ISBN (Print)9783030597214
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Breast tumor cell dataset
  • Cell counting
  • Cell detection

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