Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network
Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebr...
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doaj-616ae66dddef4ee3a025f7c4af3ab6632020-11-25T00:40:03ZengMDPI AGApplied Sciences2076-34172019-08-01916336210.3390/app9163362app9163362Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural NetworkShang Shang0Ling Long1Sijie Lin2Fengyu Cong3School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No.2 Linggong Street, Dalian 116024, ChinaCollege of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai Institute of Pollution Control and Ecological Security, Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCollege of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai Institute of Pollution Control and Ecological Security, Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No.2 Linggong Street, Dalian 116024, ChinaZebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.https://www.mdpi.com/2076-3417/9/16/3362zebrafish eggmicroscopy image processingconvolutional neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shang Shang Ling Long Sijie Lin Fengyu Cong |
spellingShingle |
Shang Shang Ling Long Sijie Lin Fengyu Cong Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network Applied Sciences zebrafish egg microscopy image processing convolutional neural network |
author_facet |
Shang Shang Ling Long Sijie Lin Fengyu Cong |
author_sort |
Shang Shang |
title |
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network |
title_short |
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network |
title_full |
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network |
title_fullStr |
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network |
title_full_unstemmed |
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network |
title_sort |
automatic zebrafish egg phenotype recognition from bright-field microscopic images using deep convolutional neural network |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-08-01 |
description |
Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis. |
topic |
zebrafish egg microscopy image processing convolutional neural network |
url |
https://www.mdpi.com/2076-3417/9/16/3362 |
work_keys_str_mv |
AT shangshang automaticzebrafisheggphenotyperecognitionfrombrightfieldmicroscopicimagesusingdeepconvolutionalneuralnetwork AT linglong automaticzebrafisheggphenotyperecognitionfrombrightfieldmicroscopicimagesusingdeepconvolutionalneuralnetwork AT sijielin automaticzebrafisheggphenotyperecognitionfrombrightfieldmicroscopicimagesusingdeepconvolutionalneuralnetwork AT fengyucong automaticzebrafisheggphenotyperecognitionfrombrightfieldmicroscopicimagesusingdeepconvolutionalneuralnetwork |
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1725291696767369216 |