Implicit Neural Representations for Generative Modeling of Living Cell Shapes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00125774" target="_blank" >RIV/00216224:14330/22:00125774 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-16440-8_6" target="_blank" >http://dx.doi.org/10.1007/978-3-031-16440-8_6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-16440-8_6" target="_blank" >10.1007/978-3-031-16440-8_6</a>
Alternative languages
Result language
angličtina
Original language name
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Medical Image Computing and Computer Assisted Intervention
ISBN
9783031164392
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
58-67
Publisher name
Springer Nature Switzerland
Place of publication
Switzerland
Event location
Singapore
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000867306400006