The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Open accessAbstract
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.
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Cited by
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
Self-citationNuClick: A deep learning framework for interactive segmentation of microscopic images
Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101771 · Cited by 147
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Metadata
- Authors
- Vu, Q. D., Graham, S., Kurc, T., To, M. N. N., Shaban, M., Qaiser, T., Koohbanani, N. A., Khurram, S. A., Kalpathy-Cramer, J., Zhao, T., Gupta, R., Kwak, J. T., Rajpoot, N., Saltz, J., & Farahani, K.
- Venue
- Frontiers in Bioengineering and Biotechnology
- Publisher
- Frontiers Media SA
- Published
- 2019-04-02
- Volume
- 7
- Pages
- 53-53
- ISSN
- 2296-4185 (ISSN-L)
- OpenAlex
- W2899488262
- Cited by
- 336
- References
- 47
- FWCI
- 14.50
- Type
- journal article
- Language
- en
- Open access
- gold
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- Vu, Q. D., Graham, S., Kurc, T., To, M. N. N., Shaban, M., Qaiser, T., Koohbanani, N. A., Khurram, S. A., Kalpathy-Cramer, J., Zhao, T., Gupta, R., Kwak, J. T., Rajpoot, N., Saltz, J., & Farahani, K. (2019). Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2019.00053
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