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Your search keyword '"Katoh, Kaoru"' showing total 11 results
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11 results on '"Katoh, Kaoru"'

5. RNaseH2A downregulation drives inflammatory gene expression via genomic DNA fragmentation in senescent and cancer cells

Author

Sugawara, Sho, Okada, Ryo, Loo, Tze Mun, Tanaka, Hisamichi, Miyata, Kenichi, Chiba, Masatomo, Kawasaki, Hiroko, Katoh, Kaoru, Kaji, Shizuo, Maezawa, Yoshiro, Yokote, Koutaro, Nakayama, Mizuho, Oshima, Masanobu, Nagao, Koji, Obuse, Chikashi, Nagayama, Satoshi, Takubo, Keiyo, Nakanishi, Akira, Kanemaki, Masato T., Hara, Eiji, and Takahashi, Akiko

Published
2022
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7. CHLOROPLAST UNUSUAL POSITIONING 1 is a plant-specific actin polymerization factor regulating chloroplast movement

Author

Kong, Sam-Geun, primary, Yamazaki, Yosuke, additional, Shimada, Atsushi, additional, T Kijima, Saku, additional, Hirose, Keiko, additional, Katoh, Kaoru, additional, Ahn, Jeongsu, additional, Song, Hyun-Geun, additional, Han, Jae-Woo, additional, Higa, Takeshi, additional, Takano, Akira, additional, Nakamura, Yuki, additional, Suetsugu, Noriyuki, additional, Kohda, Daisuke, additional, Uyeda, Taro Q P, additional, and Wada, Masamitsu, additional

Published
2023
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11. Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.

Author

Shimasaki K, Okemoto-Nakamura Y, Saito K, Fukasawa M, Katoh K, and Hanada K

Subjects
Humans, HeLa Cells, Deep Learning, Organelles metabolism, Microscopy, Phase-Contrast methods, Image Processing, Computer-Assisted methods
Abstract

Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image features. Nevertheless, rapidly growing artificial intelligence technology is overcoming obstacles. We previously reported the fine-tuned apodized phase-contrast microscopy system to capture high-resolution, label-free images of organelle dynamics in unstained living cells (Shimasaki, K. et al. (2024). Cell Struct. Funct., 49: 21-29). We here showed machine learning-based segmentation models for subcellular targeted objects in phase-contrast images using fluorescent markers as origins of ground truth masks. This method enables accurate segmentation of organelles in high-resolution phase-contrast images, providing a practical framework for studying cellular dynamics in unstained living cells.Key words: label-free imaging, organelle dynamics, apodized phase contrast, deep learning-based segmentation.

Published
2024
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