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@inproceedings{1851540,
author = {Wiesner, David and Suk, Julian and Dummer, Sven and Svoboda, David and Wolterink, Jelmer},
address = {Switzerland},
booktitle = {International Conference on Medical Image Computing and Computer Assisted Intervention},
doi = {http://dx.doi.org/10.1007/978-3-031-16440-8_6},
editor = {Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li},
keywords = {cell shape modeling; neural networks; implicit neural representations; signed distance function; generative model; interpolation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Switzerland},
isbn = {978-3-031-16439-2},
pages = {58-67},
publisher = {Springer Nature Switzerland},
title = {Implicit Neural Representations for Generative Modeling of Living Cell Shapes},
url = {http://dx.doi.org/10.1007/978-3-031-16440-8_6},
year = {2022}
}
TY - JOUR
ID - 1851540
AU - Wiesner, David - Suk, Julian - Dummer, Sven - Svoboda, David - Wolterink, Jelmer
PY - 2022
TI - Implicit Neural Representations for Generative Modeling of Living Cell Shapes
PB - Springer Nature Switzerland
CY - Switzerland
SN - 9783031164392
KW - cell shape modeling
KW - neural networks
KW - implicit neural representations
KW - signed distance function
KW - generative model
KW - interpolation
UR - http://dx.doi.org/10.1007/978-3-031-16440-8_6
N2 - Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative models for cell shape synthesis require a light-weight and flexible representation of the cell shape. However, commonly used voxel-based representations are unsuitable for high-resolution shape synthesis, and polygon meshes have limitations when modeling topology changes such as cell growth or mitosis. In this work, we propose to use level sets of signed distance functions (SDFs) to represent cell shapes. We optimize a neural network as an implicit neural representation of the SDF value at any point in a 3D+time domain. The model is conditioned on a latent code, thus allowing the synthesis of new and unseen shape sequences. We validate our approach quantitatively and qualitatively on C. elegans cells that grow and divide, and lung cancer cells with growing complex filopodial protrusions. Our results show that shape descriptors of synthetic cells resemble those of real cells, and that our model is able to generate topologically plausible sequences of complex cell shapes in 3D+time.
ER -
WIESNER, David, Julian SUK, Sven DUMMER, David SVOBODA a Jelmer WOLTERINK. Implicit Neural Representations for Generative Modeling of Living Cell Shapes. Online. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li. \textit{International Conference on Medical Image Computing and Computer Assisted Intervention}. Switzerland: Springer Nature Switzerland, 2022, s.~58-67. ISBN~978-3-031-16439-2. Dostupné z: https://dx.doi.org/10.1007/978-3-031-16440-8\_{}6.
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