Application of Deep Learning in Segmentation of Cell Image by Optical Microscope

被引：0

作者：

Jia Ce ^{[1
,2
]}

Cao Guang-Fu ^{[1
,3
]}

Wang Xiao-Feng ^{[1
]}

Zhang Xiang ^{[2
]}

机构：

[1] Guangzhou Univ, Guangzhou 510006, Peoples R China

[2] Chinese Acad Sci, Inst Biophys, Beijing 100101, Peoples R China

[3] South China Agr Univ, Guangzhou 510642, Peoples R China

来源：

PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS | 2022年 / 49卷 / 02期

关键词：

deep learning; cell segmentation; CONVOLUTIONAL NEURAL-NETWORKS;

D O I：

10.16476/j.pibb.2021.0001

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Objective In order to analyze the number and morphology of cells in the process of cell culture conveniently Methods In this paper, we introduce a cell counting method which can directly count cells in culture dish from images of commercial optical microscope by applying deep learning technology. Results In order to implement cell segmentation and counting, labeling and training is carried out on the image of adherent cells and suspension cells by a U-Net structure network. The cell growth curve is plotted and the inhibition rate of inhibitor is calculated by this algorithm, which shows the practicability of the algorithm. Conclusion It is feasible to do cell segmentation in dish by deep learning method.

引用

页码：395 / 400

页数：6

共 17 条

[1] A Review on Methods for Automatic Counting of Objects in Digital Images [J].

Barbedo, J. G. A. .

IEEE LATIN AMERICA TRANSACTIONS, 2012, 10 (05) :2112-2124

[2] A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation [J].

Hammouche, Kamal ;

Diaf, Moussa ;

Siarry, Patrick .

COMPUTER VISION AND IMAGE UNDERSTANDING, 2008, 109 (02) :163-175

[3] The Application of Wavelet Transform and the Adaptive Threshold Segmentation in Image Change Detection [J].

He, Zeng ;

Zhang, Zhongwei ;

Feng, Han ;

Wang, Long .

ADVANCES IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY, 2013, 709 :547-550

[4] The Research of Interoperability in Spatial Catalogue Service between CSW and THREDDS [J].

Hu, Chengfang ;

Di, Liping ;

Yang, Wenli .

2009 17TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, VOLS 1 AND 2, 2009, :542-+

[5] Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential [J].

Irshad, Humayun ;

Veillard, Antoine ;

Roux, Ludovic ;

Racoceanu, Daniel .

IEEE Reviews in Biomedical Engineering, 2014, 7 :97-114

[6]

Kingma DP., 2014, ACS SYM SER

[7] A survey on deep learning in medical image analysis [J].

Litjens, Geert ;

Kooi, Thijs ;

Bejnordi, Babak Ehteshami ;

Setio, Arnaud Arindra Adiyoso ;

Ciompi, Francesco ;

Ghafoorian, Mohsen ;

van der Laak, Jeroen A. W. M. ;

van Ginneken, Bram ;

Sanchez, Clara I. .

MEDICAL IMAGE ANALYSIS, 2017, 42 :60-88

[8] Robust plant cell tracking using local spatio-temporal context [J].

Liu, Min ;

Xiang, Peng ;

Liu, Guocai .

NEUROCOMPUTING, 2016, 208 :309-314

[9] Incorporating spatial dependence in predictive vegetation models [J].

Miller, Jennifer ;

Franklin, Janet ;

Aspinall, Richard .

ECOLOGICAL MODELLING, 2007, 202 (3-4) :225-242

[10] Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images [J].

Pereira, Sergio ;

Pinto, Adriano ;

Alves, Victor ;

Silva, Carlos A. .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) :1240-1251

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