Transfer Learning in Optical Microscopy

D 2021

Transfer Learning in Optical Microscopy

KOZLOVSKÝ, Martin, David WIESNER and David SVOBODA

Basic information

Original name

Transfer Learning in Optical Microscopy

Authors

KOZLOVSKÝ, Martin (203 Czech Republic, belonging to the institution), David WIESNER (203 Czech Republic, belonging to the institution) and David SVOBODA (203 Czech Republic, guarantor, belonging to the institution)

Edition

LNCS 12965. Neuveden, Simulation and Synthesis in Medical Imaging, p. 77-86, 10 pp. 2021

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/21:00122077

Organization unit

Faculty of Informatics

ISBN

978-3-030-87591-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-87592-3_8

UT WoS

001116104600008

Keywords in English

Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning

Abstract

V originále

Image synthesis is nowadays a very rapidly evolving branch of deep learning. One of possible applications of image synthesis is an image-to-image translation. There is currently a lot of focus orientated to applications of image translation in medicine, mainly involving translation between different screening techniques. One of other possible use of image translation in medicine and biology is in the task of translation between various imaging techniques in modern microscopy. In this paper, we propose a novel method based on DenseNet architecture and we compare it with Pix2Pix model in the task of translation from images imaged using phase-contrast technique to fluorescence images with focus on usability for cell segmentation.

Links

MUNI/A/1108/2020, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace X. (Acronym: SV-FI MAV X.)

Investor: Masaryk University

Citovat

KOZLOVSKÝ, Martin, David WIESNER and David SVOBODA. Transfer Learning in Optical Microscopy. Online. In Svoboda D., Burgos N., Wolterink J., Zhao C. Simulation and Synthesis in Medical Imaging. LNCS 12965. Neuveden: Springer, 2021, p. 77-86. ISBN 978-3-030-87591-6. Available from: https://dx.doi.org/10.1007/978-3-030-87592-3_8.

@inproceedings{1785739,
   author = {Kozlovský, Martin and Wiesner, David and Svoboda, David},
   address = {Neuveden},
   booktitle = {Simulation and Synthesis in Medical Imaging},
   doi = {http://dx.doi.org/10.1007/978-3-030-87592-3_8},
   edition = {LNCS 12965},
   editor = {Svoboda D., Burgos N., Wolterink J., Zhao C.},
   keywords = {Fluorescence microscopy; Phase-contrast microscopy; GAN; Image synthesis; Machine learning},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-3-030-87591-6},
   pages = {77-86},
   publisher = {Springer},
   title = {Transfer Learning in Optical Microscopy},
   url = {https://2021.sashimi-workshop.org/},
   year = {2021}
}

TY  - JOUR
ID  - 1785739
AU  - Kozlovský, Martin - Wiesner, David - Svoboda, David
PY  - 2021
TI  - Transfer Learning in Optical Microscopy
PB  - Springer
CY  - Neuveden
SN  - 9783030875916
KW  - Fluorescence microscopy
KW  - Phase-contrast microscopy
KW  - GAN
KW  - Image synthesis
KW  - Machine learning
UR  - https://2021.sashimi-workshop.org/
N2  - Image synthesis is nowadays a very rapidly evolving branch of deep learning. One of possible applications of image synthesis is an image-to-image translation. There is currently a lot of focus orientated to applications of image translation in medicine, mainly involving translation between different screening techniques. One of other possible use of image translation in medicine and biology is in the task of translation between various imaging techniques in modern microscopy. In this paper, we propose a novel method based on DenseNet architecture and we compare it with Pix2Pix model in the task of translation from images imaged using phase-contrast technique to fluorescence images with focus on usability for cell segmentation.
ER  -

KOZLOVSKÝ, Martin, David WIESNER and David SVOBODA. Transfer Learning in Optical Microscopy. Online. In Svoboda D., Burgos N., Wolterink J., Zhao C. \textit{Simulation and Synthesis in Medical Imaging}. LNCS 12965. Neuveden: Springer, 2021, p.~77-86. ISBN~978-3-030-87591-6. Available from: https://dx.doi.org/10.1007/978-3-030-87592-3\_{}8.

Detailed Information on Publication Record