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
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_43
Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.A.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: AAAI (2016)
Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51
Dhall, D., et al.: Ki-67 proliferative index predicts progression-free survival of patients with well-differentiated ileal neuroendocrine tumors. Hum. Pathol. 43(4), 489–495 (2012)
Hagos, Y.B., Narayanan, P.L., Akarca, A.U., Marafioti, T., Yuan, Y.: ConCORDe-Net: cell count regularized convolutional neural network for cell detection in multiplex immunohistochemistry images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 667–675. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_74
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hou, L., et al.: Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pat. Recog. 86, 188–200 (2019)
Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 276–283. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_33
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: NeurIPS, pp. 1324–1332 (2010)
Li, C., Wang, X., Liu, W., Latecki, L.J., Wang, B., Huang, J.: Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med. Image Anal. 53, 165–178 (2019)
Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: CVPR, pp. 1091–1100 (2018)
Ram, S., Rodriguez, J.J.: Size-invariant detection of cell nuclei in microscopy images. IEEE Trans. Med. Imaging 35(7), 1753–1764 (2016)
Rojas-Moraleda, R., et al.: Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework. Med. Image Anal. 38, 90–103 (2017)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA: A Cancer J. Clin. 69(1), 7–34 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Tofighi, M., Guo, T., Vanamala, J.K., Monga, V.: Prior information guided regularized deep learning for cell nucleus detection. IEEE Trans. Med. Imaging 38, 2047–2058 (2019)
Veta, M., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237–248 (2015)
Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 358–365. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_43
Xie, Y., Xing, F., Shi, X., Kong, X., Su, H., Yang, L.: Efficient and robust cell detection: a structured regression approach. Med. Image Anal. 44, 245–254 (2018)
Xu, J., et al.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2015)
Xue, Y., Bigras, G., Hugh, J., Ray, N.: Training convolutional neural networks and compressed sensing end-to-end for microscopy cell detection. IEEE Trans. Med. Imaging 38, 2632–2641 (2019)
Yellin, F., Haeffele, B.D., Roth, S., Vidal, R.: Multi-cell detection and classification using a generative convolutional model. In: CVPR, pp. 8953–8961 (2018)
Zhang, A., Shen, J., Xiao, Z., Zhu, F., Zhen, X., Cao, X., Shao, L.: Relational attention network for crowd counting. In: ICCV, pp. 6788–6797 (2019)
Zhou, Y., Dou, Q., Chen, H., Qin, J., Heng, P.A.: SFCN-OPI: detection and fine-grained classification of nuclei using sibling FCN with objectness prior interaction. In: AAAI (2018)
Acknowledgements
This work is supported by the Nature Science Foundation of China (No. 62081360152, No. 61972217), Natural Science Foundation of Guangdong Province in China (No. 2019B1515120049, 2020B1111340056).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Z. et al. (2020). BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-59722-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59721-4
Online ISBN: 978-3-030-59722-1
eBook Packages: Computer ScienceComputer Science (R0)
