Skip to main content

Advertisement

SpringerLink
Log in

BIS5k: a large-scale dataset for medical segmentation task based on HE-staining images of breast cancer

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Breast cancer, a high-incidence cancer among female, occupies a large incidence of total female patients with cancer. Pathological examination is the gold standard for breast cancer in clinic diagnosis. However, accuracy and efficient diagnosis is challengeable to pathologists for the complex of breast cancer and laborious work. Introducing computer-aid diagnosis (CAD) can relieve laborious work of pathologists and improve diagnosed accuracy for breast cancer. To promote development of CAD methods, we release a large-scale and hematoxylin-eosin (HE) staining dataset of breast cancer for medical image segmentation task, called the breast-cancer image segmentation 5000 (BIS5k). BIS5k contains 5929 images that are divided into training data (5000) and evaluated data (929). All images of BIS5k are collected from clinic cases which include patients with various age and cancer stages. All labels of images are annotated in pixel level for segmentation task and reviewed by pathological professors carefully. Furthermore, we construct a basic instance called breast-cancer segmentation network, BCSNet with a toolkit including comprehensive metrics to demonstrate the usage of BIS5k. Extensive experiments of BCSNet and compared methods provide that developing specific algorithm and constructing dataset are indispensable to promote CAD of pathological diagnosis for breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Araujo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), 0177544 (2017)

    Article  Google Scholar 

  2. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567 (2016)

  3. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2015)

    Article  Google Scholar 

  4. Bayramoglu, N., Kannala, J., Heikkilä, J.: Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2440–2445 (2016)

  5. Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., Loo, R., Vogels, R.: 1399 he-stained sentinel lymph node sections of breast cancer patients: the Camelyon dataset. GigaScience 7(6), 065 (2018)

    Article  Google Scholar 

  6. Pati, P., Jaume, G., Foncubierta-Rodriguez, A., Feroce, F., Anniciello, A.M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D.: Hierarchical graph representations in digital pathology. Med. Image Anal. 75, 102264 (2022)

    Article  Google Scholar 

  7. Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N., Nelson, H.D., Pepe, M.S., Allison, K.H., Schnitt, S.J.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)

    Article  Google Scholar 

  8. Belsare, A., Mushrif, M., Pangarkar, M., Meshram, N.: Classification of breast cancer histopathology images using texture feature analysis. In: Tencon 2015–2015 IEEE Region 10 Conference, pp. 1–5 (2015)

  9. Lei, B., Huang, S., Li, R., Bian, C., Li, H., Chou, Y.-H., Cheng, J.-Z.: Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder–decoder network. Neurocomputing 321, 178–186 (2018)

    Article  Google Scholar 

  10. Hirsch, L., Huang, Y., Luo, S., Saccarelli, C.R., Gullo, R.L., Naranjo, I.D., Bitencourt, A.G., Onishi, N., Ko, E.S., Leithner, D.: Deep learning achieves radiologist-level performance of tumor segmentation in breast MRI (2020). arXiv preprint arXiv:2009.09827

  11. Deniz, E., Şengür, A., Kadiroğlu, Z., Guo, Y., Bajaj, V., Budak, U.: Transfer learning based histopathologic image classification for breast cancer detection. Health Inf. Sci. Syst. 6, 1–7 (2018)

    Article  Google Scholar 

  12. Yao, H., Zhang, X., Zhou, X., Liu, S.: Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification. Cancers 11(12), 1901 (2019)

    Article  Google Scholar 

  13. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241 (2015)

  14. Lou, A., Guan, S., Loew, M.: Caranet: context axial reverse attention network for segmentation of small medical objects. J. Med. Imaging 10(1), 014005–014005 (2023)

    Article  Google Scholar 

  15. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

  16. Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548–4557 (2017)

  17. Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, 107404 (2020)

    Article  Google Scholar 

  18. Kim, T., Lee, H., Kim, D.: Uacanet: uncertainty augmented context attention for polyp segmentation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2167–2175 (2021)

  19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

Download references

Funding

This work was funded by the National Natural Science Foundation of China (Grant No.32100530), Natural Science Foundation of Chongqing, China (Grant No. CSTB2022BSXM-JCX0065), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202200455). This work was also supported by Graduate Research and Innovation Fund of Yunnan University under Grants KC-22221913.

Author information

Authors and Affiliations

  1. Department of Pathology, Chongqing Medical University, Chongqing, 400016, Chongqing, China

    Junjie Li & Lingyu Li

  2. School of Information and Engineering, Yunnan University, Kunming, 650500, Yunnan, China

    Kaixiang Yan

  3. Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing, 400016, Chongqing, China

    Yu Yu & Lingyu Li

  4. Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, Chongqing, China

    Yu Yu & Lingyu Li

  5. Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, Chongqing, China

    Xiaohui Zhan

  6. Center for Medical Epigenetics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400016, Chongqing, China

    Xiaohui Zhan

Authors
  1. Junjie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaixiang Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaohui Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lingyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Junjie Li and Kaixiang Yan wrote the main manuscript text. Yu Yu and Junjie Li prepared data. Lingyu Li provided primary ideal. Lingyu Li and Xiaohui Zhan proofread the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Lingyu Li.

Ethics declarations

Conflict of interest

To the best of our knowledge, the named authors have no conflict of interest, financial, or otherwise.

Ethical approval

No human or animal experiments are involved in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Junjie Li and Kaixiang Yan contributted equally for this work.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Yan, K., Yu, Y. et al. BIS5k: a large-scale dataset for medical segmentation task based on HE-staining images of breast cancer. SIViP 18, 3705–3713 (2024). https://doi.org/10.1007/s11760-024-03034-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03034-2

Keywords

Search

Navigation