Biopolymer segmentation from CLSM microscopy images using a convolutional neural network
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
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Proceedings in applied mathematics and mechanics : PAMM. - 20, 1 (2021) , e202000188, ISSN: 1617-7061
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2024
- Urheber
- DOI
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10.1002/pamm.202000188
- URN
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urn:nbn:de:bsz:25-freidok-2483026
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:51 MESZ
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Beteiligte
- Asgharzadeh, Pouyan
- Birkhold, Annette Isabell
- Özdemir, Bugra
- Reski, Ralf
- Röhrle, Oliver
- Universität
Entstanden
- 2024
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