Biopolymer segmentation from CLSM microscopy images using a convolutional neural network. Issue 1 (25th January 2021)

• Record Type: Journal Article
• Authors: Asgharzadeh, Pouyan; Birkhold, Annette I.; Özdemir, Bugra; Reski, Ralf; Röhrle, Oliver
• Other Names: Kuhl D. guestEditor.; Meister A. guestEditor.; Ricoeur A. guestEditor.; Wünsch O. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 20:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-25
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202000188
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: 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.