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@article{1354015,
author = {Svoboda, David and Ulman, Vladimír},
article_number = {1},
doi = {http://dx.doi.org/10.1109/TMI.2016.2606545},
keywords = {Simulation; Molecular and cellular imaging; Microscopy; Cell; Image synthesis},
language = {eng},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
title = {MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy},
url = {http://dx.doi.org/10.1109/TMI.2016.2606545},
volume = {36},
year = {2017}
}
TY - JOUR
ID - 1354015
AU - Svoboda, David - Ulman, Vladimír
PY - 2017
TI - MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy
JF - IEEE Transactions on Medical Imaging
VL - 36
IS - 1
SP - 310-321
EP - 310-321
PB - IEEE Engineering in Medicine and Biology Society
SN - 02780062
KW - Simulation
KW - Molecular and cellular imaging
KW - Microscopy
KW - Cell
KW - Image synthesis
UR - http://dx.doi.org/10.1109/TMI.2016.2606545
N2 - The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.
ER -
SVOBODA, David and Vladimír ULMAN. MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy. \textit{IEEE Transactions on Medical Imaging}. IEEE Engineering in Medicine and Biology Society, 2017, vol.~36, No~1, p.~310-321. ISSN~0278-0062. Available from: https://dx.doi.org/10.1109/TMI.2016.2606545.
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