The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map
Abstract
The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new method to automatically segment nuclei from Haematoxylin and Eosin (H&E) stained histopathology data with fully convolutional networks. In particular, we address the problem of segmenting touching nuclei by formulating the segmentation problem as a regression task of the distance map. We demonstrate superior performance of this approach as compared to other approaches using Convolutional Neural Networks.
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Cited by
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729
A Multi-Organ Nucleus Segmentation Challenge
Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P.-A., Li, J., Hu, Z., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2947628 · Cited by 553
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
Metadata
- Authors
- Naylor, P., Laé, M., Reyal, F., & Walter, T.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2019-02
- Volume
- 38
- Issue
- 2
- Pages
- 448-459
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2885343725
- Cited by
- 685
- References
- 47
- FWCI
- 27.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Naylor, P., Laé, M., Reyal, F., & Walter, T. (2019). Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2018.2865709
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