Nuclei Segmentation in Histopathology Images


In the field of computational histopathology, nuclei segmentation is one of the important tasks. In the following works, we evaluate:

Example Nuclei Segmentation with color decomposition active contours [1]


[1] V. B. S. Prasath, K. Fukuma, B. J. Aronow, H. Kawanaka. Cell nuclei segmentation in glioma histopathology images with color decomposition based active contours. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), Washington, DC, USA. Proc. IEEE. pp. 1734-1736, Nov 2015. doi:10.1109/BIBM.2015.7359944. Poster available at figshare: 10.6084/m9.figshare.1588805

[2] K. Fukuma, V. B. S. Prasath, H. Kawanaka, B. J. Aronow, H. Takase. A study on nuclei segmentation, feature extraction and disease stage classification for human brain histopathological images. International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), York, UK, Sep 2016. Procedia Computer Science, 96, pp. 1202-1020, Sep 2016. doi:10.1016/j.procs.2016.08.164

[3] T. Hayakawa, V. B. S. Prasath, H. Kawanaka, B. J. Aronow, S. Tsuruoka. Computational nuclei segmentation methods in digital pathology - A survey. Archives of Computational Methods in Engineering, 28(1), 1-13, January 2021. doi:10.1007/s11831-019-09366-4

In preparation:

Nuclei segmentation and feature extraction for disease stage classification in Glioblastoma Multiforme.

Automatic nuclei segmentation in glioma histopathology with adaptive filtering based entropy thresholding.

MS Thesis:

Tomohiro Hayakawa. Nuclei detection and segmentation in glioma histopathology images. Graduate School of Engineering, Mie University, Japan, 2017.

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