Abstract

We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods. In the approach, we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis. As a validation, we demonstrated the performances of the network trained by phase images and intensity images, respectively. The convolutional neural network trained by phase images achieved an average accuracy of 98.9% on the validation data, which outperforms the average accuracy 89.6% obtained by the network trained by intensity images. We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.

© 2021 Chinese Laser Press

Classification of cell morphology with quantitative phase microscopy and machine learning

Ying Li, Jianglei Di, Kaiqiang Wang, Sufang Wang, and Jianlin Zhao
Opt. Express 28(16) 23916-23927 (2020)

Deep-learning based flat-fielding quantitative phase contrast microscopy

Wenjian Wang, Kequn Zhuo, Xin Liu, Wenjing Feng, Zihan Xiong, Ruihua Liu, Nauman Ali, Ying Ma, Juanjuan Zheng, Sha An, and Peng Gao
Opt. Express 32(7) 12462-12475 (2024)

Single-shot quantitative phase contrast imaging based on deep learning

Yu-Chun Lin, Yuan Luo, Ying-Ju Chen, Huei-Wen Chen, Tai-Horng Young, and Hsuan-Ming Huang
Biomed. Opt. Express 14(7) 3458-3468 (2023)

Cytopathological image analysis using deep-learning networks in microfluidic microscopy

G. Gopakumar, K. Hari Babu, Deepak Mishra, Sai Siva Gorthi, and Gorthi. R. K. Sai Subrahmanyam
J. Opt. Soc. Am. A 34(1) 111-121 (2017)

Quantitative phase gradient metrology using diffraction phase microscopy and deep learning

Allaparthi Venkata Satya Vithin and Rajshekhar Gannavarpu
J. Opt. Soc. Am. A 40(3) 611-619 (2023)