The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Breast Cancer Histopathology Image Analysis: A Review
Abstract
This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.
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Citations
Cites
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Wolff, A. C., Hammond, M. E. H., Schwartz, J. N., Hagerty, K. L., Allred, D. C., Cote, R. J., Dowsett, M., Fitzgibbons, P. L., Hanna, W. M., Langer, A., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.09.2775 · Cited by 4,434
Tissue microarrays for high-throughput molecular profiling of tumor specimens
Kononen, J., Bubendorf, L., Kallionimeni, A., Bärlund, M., Schraml, P., Leighton, S., Torhorst, J., Mihatsch, M. J., Sauter, G., & Kallionimeni, O.-P. (1998) · Nature Medicine · doi:10.1038/nm0798-844 · Cited by 4,099
Histopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052
Quantification of histochemical staining by color deconvolution.
Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029
Dowsett, M., Nielsen, T. O., A'Hern, R., Bartlett, J., Coombes, R. C., Cuzick, J., Ellis, M., Henry, N. L., Hugh, J. C., Lively, T., et al. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr393 · Cited by 1,977
Wolff, A. C., Hammond, M. E. H., Schwartz, J. N., Hagerty, K. L., Allred, D. C., Cote, R. J., Dowsett, M., Fitzgibbons, P. L., Hanna, W. M., Langer, A., et al. (2007) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2007-131-18-asocco · Cited by 1,816
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536
A method for normalizing histology slides for quantitative analysis
Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193250 · Cited by 1,482
Prognostic Factors in Breast Cancer: College of American Pathologists Consensus Statement 1999
Fitzgibbons, P. L., Page, D. L., Weaver, D., Thor, A. D., Allred, D. C., Clark, G. M., Ruby, S. G., O'Malley, F., Simpson, J. F., Connolly, J. L., et al. (2000) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2000-124-0966-pfibc · Cited by 1,016
Cited by
Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,013
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2458702 · Cited by 854
Khan, S., Islam, N., Jan, Z., Ud Din, I., & Rodrigues, J. J. P. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.022 · Cited by 785
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., Tomaszewski, J., González, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 532
Metadata
- Authors
- Veta, M., Pluim, J. P. W., van Diest, P. J., & Viergever, M. A.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2014-05
- Volume
- 61
- Issue
- 5
- Pages
- 1400-1411
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2151608510
- Cited by
- 696
- References
- 82
- FWCI
- 38.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Veta, M., Pluim, J. P. W., van Diest, P. J., & Viergever, M. A. (2014). Breast Cancer Histopathology Image Analysis: A Review. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2014.2303852
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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