Generative modeling of living cells with SO(3)-equivariant implicit neural representations.

Autor: Wiesner D; Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic. Electronic address: wiesner@fi.muni.cz., Suk J; Department of Applied Mathematics & Technical Medical Centre, University of Twente, Enschede, The Netherlands., Dummer S; Department of Applied Mathematics & Technical Medical Centre, University of Twente, Enschede, The Netherlands., Nečasová T; Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic., Ulman V; IT4Innovations, VSB - Technical University of Ostrava, Ostrava, Czech Republic., Svoboda D; Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic., Wolterink JM; Department of Applied Mathematics & Technical Medical Centre, University of Twente, Enschede, The Netherlands. Electronic address: j.m.wolterink@utwente.nl.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Jan; Vol. 91, pp. 102991. Date of Electronic Publication: 2023 Oct 05.
DOI: 10.1016/j.media.2023.102991
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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