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@inproceedings{1546941,
author = {Wiesner, David and Nečasová, Tereza and Svoboda, David},
address = {Trento},
booktitle = {Image Analysis and Processing – ICIAP 2019},
doi = {http://dx.doi.org/10.1007/978-3-030-30645-8_61},
edition = {LNCS 11752},
editor = {Ricci Elisa, Rota Buló Samuel, Snoek Cees, Lanz Oswald, Messelodi Stefano, Sebe Nicu},
keywords = {Image-based Simulations; 3D GAN; Training Stability; Microscopy Data; Digital Cell Shape},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Trento},
isbn = {978-3-030-30644-1},
pages = {672-682},
publisher = {Springer},
title = {On Generative Modeling of Cell Shape Using 3D GANs},
url = {http://dx.doi.org/10.1007/978-3-030-30645-8_61},
year = {2019}
}
TY - JOUR
ID - 1546941
AU - Wiesner, David - Nečasová, Tereza - Svoboda, David
PY - 2019
TI - On Generative Modeling of Cell Shape Using 3D GANs
PB - Springer
CY - Trento
SN - 9783030306441
KW - Image-based Simulations
KW - 3D GAN
KW - Training Stability
KW - Microscopy Data
KW - Digital Cell Shape
UR - http://dx.doi.org/10.1007/978-3-030-30645-8_61
N2 - The ongoing advancement of deep-learning generative models, showing great interest of the scientific community since the introduction of the generative adversarial networks (GAN), paved the way for generation of realistic data. The utilization of deep learning for the generation of realistic biomedical images allows one to alleviate the constraints of the parametric models, limited by the employed mathematical approximations. Building further upon the laid foundation, the 3D GAN added another dimension, allowing generation of fully 3D volumetric data. In this paper, we present an approach to generating fully 3D volumetric cell masks using GANs. Presented model is able to generate high-quality cell masks with variability matching the real data. Required modifications of the proposed model are presented along with the training dataset, based on 385 real cells captured using the fluorescence microscope. Furthermore, the statistical validation is also presented, allowing to quantitatively assess the quality of data generated by the proposed model.
ER -
WIESNER, David, Tereza NEČASOVÁ a David SVOBODA. On Generative Modeling of Cell Shape Using 3D GANs. Online. In Ricci Elisa, Rota Buló Samuel, Snoek Cees, Lanz Oswald, Messelodi Stefano, Sebe Nicu. \textit{Image Analysis and Processing – ICIAP 2019}. LNCS 11752. Trento: Springer, 2019, s.~672-682. ISBN~978-3-030-30644-1. Dostupné z: https://dx.doi.org/10.1007/978-3-030-30645-8\_{}61.
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