The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning
Abstract
In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.
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Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937
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
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map
Naylor, P., Laé, M., Reyal, F., & Walter, T. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2865709 · Cited by 685
Metadata
- Authors
- Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., & Wang, T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-10
- Volume
- 62
- Issue
- 10
- Pages
- 2421-2433
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1582640985
- Cited by
- 273
- References
- 38
- FWCI
- 13.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., & Wang, T. (2015). Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2015.2430895
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