PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Hang, Z.* ; Shiwei, L.* ; Qing, H.* ; Shijie, L.* ; Tingwei, Q.* ; Cai, R.* ; Ertürk, A. ; Shaoqun, Z.*

A 3D high resolution generative deep-learning network for fluorescence microscopy image.

bioRxiv (2019)
Deep learning technology enables us acquire high resolution image from low resolution image in biological imaging free from sophisticated optical hardware. However, current methods require a huge number of the precisely registered low-resolution (LR) and high-resolution (HR) volume image pairs. This requirement is challengeable for biological volume imaging. Here, we proposed 3D deep learning network based on dual generative adversarial network (dual-GAN) framework for recovering HR volume images from LR volume images. Our network avoids learning the direct mappings from the LR and HR volume image pairs, which need precisely image registration process. And the cycle consistent network makes the predicted HR volume image faithful to its corresponding LR volume image. The proposed method achieves the recovery of 20x/1.0 NA volume images from 5x/0.16 NA volume images collected by light-sheet microscopy. In essence our method is suitable for the other imaging modalities.
Impact Factor
Scopus SNIP
0.000
0.000
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
Zeitschrift bioRxiv
Verlag Cold Spring Harbor Laboratory Press
Verlagsort Cold Spring Harbor
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Erfassungsdatum 2019-10-23
[➜Korrektur melden]