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@inproceedings{1409298,
author = {Castilla, Carlos and Maška, Martin and Sorokin, Dmitry and Meijering, Erik and OrtizanddeandSolorzano, Carlos},
address = {Washington},
booktitle = {15th IEEE International Symposium on Biomedical Imaging},
doi = {http://dx.doi.org/10.1109/ISBI.2018.8363605},
keywords = {Cell segmentation; Convolutional Neural Networks; Chan-Vese model; Filopodia},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Washington},
isbn = {978-1-5386-3636-7},
pages = {413-417},
publisher = {IEEE},
title = {Segmentation of Actin-Stained 3D Fluorescent Cells with Filopodial Protrusions using Convolutional Neural Networks},
url = {https://doi.org/10.1109/ISBI.2018.8363605},
year = {2018}
}
TY - JOUR
ID - 1409298
AU - Castilla, Carlos - Maška, Martin - Sorokin, Dmitry - Meijering, Erik - Ortiz-de-Solorzano, Carlos
PY - 2018
TI - Segmentation of Actin-Stained 3D Fluorescent Cells with Filopodial Protrusions using Convolutional Neural Networks
PB - IEEE
CY - Washington
SN - 9781538636367
KW - Cell segmentation
KW - Convolutional Neural Networks
KW - Chan-Vese model
KW - Filopodia
UR - https://doi.org/10.1109/ISBI.2018.8363605
N2 - We present the architecture, training strategy and evaluation of a convolutional neural network (CNN) designed for the segmentation of actin-stained cells in 3D+t confocal microscopy image data. The segmentation performance of the CNN is evaluated using time-lapse sequences of lung adenocarcinoma cells with three genetically distinct variants of the tubulin adaptor protein, a key protein in the process of assembly of the cell cytoskeleton, displaying three different phenotypes in regards to the morphology of the cells and in particular, to the number and length of filopodial structures. We show that the CNN significantly outperforms a baseline method based on the minimization of the Chan-Vese model using graph cuts, and we discuss the inherent benefits of using the CNN over the baseline method.
ER -
CASTILLA, Carlos, Martin MAŠKA, Dmitry SOROKIN, Erik MEIJERING and Carlos ORTIZ-DE-SOLORZANO. Segmentation of Actin-Stained 3D Fluorescent Cells with Filopodial Protrusions using Convolutional Neural Networks. Online. In \textit{15th IEEE International Symposium on Biomedical Imaging}. Washington: IEEE, 2018, p.~413-417. ISBN~978-1-5386-3636-7. Available from: https://dx.doi.org/10.1109/ISBI.2018.8363605.
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