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Biopolymer segmentation from CLSM microscopy images using a convolutional neural network
Author(s) -
Asgharzadeh Pouyan,
Birkhold Annette I.,
Özdemir Bugra,
Reski Ralf,
Röhrle Oliver
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000188
Subject(s) - artificial intelligence , segmentation , convolutional neural network , physcomitrella patens , microscopy , computer science , confocal laser scanning microscopy , confocal microscopy , confocal , biopolymer , deep learning , visualization , pattern recognition (psychology) , computer vision , materials science , optics , biology , physics , biophysics , polymer , biochemistry , mutant , gene , composite material
Confocal microscopy allows visualization of biopolymer networks at the nano scale. Analyzing the structure and assembly of protein networks from images requires a segmentation process. This has proven to be challenging due to multiple possible sources of noise in images as well as exhibition of out‐of‐focus planes. Here, we present a deep learning‐based segmentation procedure for confocal laser scanning microscopy images of biopolymer networks. Utilizing an encoder‐decoder network architecture, our deep neural network achieved a dice score of 0.88 in segmenting images of filamentous temperature sensitive Z proteins from chloroplasts of Physcomitrella patens , a moss.