DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci
Result description
DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and gamma H2AX) and uses U-Net for nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation.
Keywords
alternative splicing variantcomplex cell responsesgamma-h2ax focichromatin-structureh2ax phosphorylationnanoscopy techniqueshistone h2ax
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/00216305:26220/21:PU142171 RIV/00216224:14110/21:00123920
Result on the web
https://www.sciencedirect.com/science/article/pii/S2001037021004840?via%3Dihub
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci
Original language description
DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and gamma H2AX) and uses U-Net for nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation.
Czech name
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Czech description
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Classification
Type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10608 - Biochemistry and molecular biology
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computational and Structural Biotechnology Journal
ISSN
2001-0370
e-ISSN
2001-0370
Volume of the periodical
19
Issue of the periodical within the volume
DEC 2021
Country of publishing house
SE - SWEDEN
Number of pages
16
Pages from-to
6465-6480
UT code for WoS article
000731411300007
EID of the result in the Scopus database
2-s2.0-85120749934
Basic information
Result type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
OECD FORD
Biochemistry and molecular biology
Year of implementation
2021