Record link: 
http://hdl.handle.net/20.500.12708/139040
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Title: 
Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation
en
Citation: 
Kromp, F., Fischer, L., Bozsaky, E., Ambros, I., Dörr, W., Beiske, K., Ambros, P., Hanbury, A., & Taschner-Mandl, S. (2021). Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation. IEEE Transactions on Medical Imaging, 40(7), 1934–1949. https://doi.org/10.1109/tmi.2021.3069558
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Publisher DOI: 
10.1109/tmi.2021.3069558
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Publication Type: 
Article - Original Research Article
en
Language: 
English
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Authors: 
Kromp, Florian 
Fischer, Lukas 
Bozsaky, Eva 
Ambros, Inge 
Dörr, Wolfgang 
Beiske, Klaus 
Ambros, Peter 
Hanbury, Allan 
Taschner-Mandl, Sabine 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
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Journal: 
IEEE Transactions on Medical Imaging 
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ISSN: 
0278-0062
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Date (published): 
2021
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Number of Pages: 
16
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Publisher: 
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Peer reviewed: 
Yes
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Keywords: 
Electrical and Electronic Engineering; Computer Science Applications; Software; Radiological and Ultrasound Technology
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
Informatik
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