Nuclei and Cell Segmentation, Counting in Histopathology Images

Abstract:

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

• Different nuclei segmentation methods - Benchmarking image processing, machine learning, and deep learning-driven nuclei segmentation models

• Test nuclei feature descriptors for disease stage classification of Gliomas

• Combining nuclei segmentation for other pan-cancer domains

• Testing object detectors, cell counting models

• Fine-tuning pathology foundation models

References:

[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

[4] S. S. Debsarkar, B. J. Aronow, V. B. S. Prasath. Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review. Computers in Biology and Medicine, 190, 110072, May 2025. doi:10.1016/j.compbiomed.2025.110072

[5] S. S. Debsarkar, B. J. Aronow, V. B. S. Prasath. Anonymous submission. Submitted, 2025.

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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