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@article{1442158,
author = {Castilla, Carlos and Maška, Martin and Sorokin, Dmitry and Meijering, Erik and OrtizanddeandSolórzano, Carlos},
article_number = {3},
doi = {http://dx.doi.org/10.1109/TMI.2018.2873842},
keywords = {Filopodium segmentation and tracking;actin cytoskeleton;confocal microscopy;3D skeletonization;Chan-Vese model;convolutional neural network;deep learning},
language = {eng},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
title = {3-D Quantification of Filopodia in Motile Cancer Cells},
url = {https://doi.org/10.1109/TMI.2018.2873842},
volume = {38},
year = {2019}
}
TY - JOUR
ID - 1442158
AU - Castilla, Carlos - Maška, Martin - Sorokin, Dmitry - Meijering, Erik - Ortiz-de-Solórzano, Carlos
PY - 2019
TI - 3-D Quantification of Filopodia in Motile Cancer Cells
JF - IEEE Transactions on Medical Imaging
VL - 38
IS - 3
SP - 862-872
EP - 862-872
PB - IEEE
SN - 02780062
KW - Filopodium segmentation and tracking;actin cytoskeleton;confocal microscopy;3D skeletonization;Chan-Vese model;convolutional neural network;deep learning
UR - https://doi.org/10.1109/TMI.2018.2873842
L2 - https://doi.org/10.1109/TMI.2018.2873842
N2 - We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.
ER -
CASTILLA, Carlos, Martin MAŠKA, Dmitry SOROKIN, Erik MEIJERING and Carlos ORTIZ-DE-SOLÓRZANO. 3-D Quantification of Filopodia in Motile Cancer Cells. \textit{IEEE Transactions on Medical Imaging}. IEEE, 2019, vol.~38, No~3, p.~862-872. ISSN~0278-0062. Available from: https://dx.doi.org/10.1109/TMI.2018.2873842.
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