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@article{2324238,
author = {Wiesner, David and Suk, Julian and Dummer, Sven and Nečasová, Tereza and Vladimír, Ulman and Svoboda, David and Wolterink, Jelmer},
article_location = {Netherlands},
article_number = {91},
doi = {http://dx.doi.org/10.1016/j.media.2023.102991},
keywords = {cell shape modeling; neural networks; implicit neural representations; signed distance function; generative model; interpolation},
language = {eng},
issn = {1361-8415},
journal = {Medical Image Analysis},
title = {Generative modeling of living cells with SO(3)-equivariant implicit neural representations},
url = {https://doi.org/10.1016/j.media.2023.102991},
volume = {2024},
year = {2024}
}
TY - JOUR
ID - 2324238
AU - Wiesner, David - Suk, Julian - Dummer, Sven - Nečasová, Tereza - Vladimír, Ulman - Svoboda, David - Wolterink, Jelmer
PY - 2024
TI - Generative modeling of living cells with SO(3)-equivariant implicit neural representations
JF - Medical Image Analysis
VL - 2024
IS - 91
SP - 102991-103008
EP - 102991-103008
PB - Elsevier
SN - 13618415
KW - cell shape modeling
KW - neural networks
KW - implicit neural representations
KW - signed distance function
KW - generative model
KW - interpolation
UR - https://doi.org/10.1016/j.media.2023.102991
N2 - Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.
ER -
WIESNER, David, Julian SUK, Sven DUMMER, Tereza NEČASOVÁ, Ulman VLADIMÍR, David SVOBODA and Jelmer WOLTERINK. Generative modeling of living cells with SO(3)-equivariant implicit neural representations. \textit{Medical Image Analysis}. Netherlands: Elsevier, 2024, vol.~2024, No~91, p.~102991-103008. ISSN~1361-8415. Available from: https://dx.doi.org/10.1016/j.media.2023.102991.
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