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Methods allowing the synthesis of realistic cell shapes could help generatetraining data sets to improve cell tracking and segmentation in biomedicalimages. Deep generative models for cell shape synthesis require a light-weightand flexible representation of the cell shape. However, commonly usedvoxel-based representations are unsuitable for high-resolution shape synthesis,and polygon meshes have limitations when modeling topology changes such as cellgrowth or mitosis. In this work, we propose to use level sets of signeddistance functions (SDFs) to represent cell shapes. We optimize a neuralnetwork as an implicit neural representation of the SDF value at any point in a3D+time domain. The model is conditioned on a latent code, thus allowing thesynthesis of new and unseen shape sequences. We validate our approachquantitatively and qualitatively on C. elegans cells that grow and divide, andlung cancer cells with growing complex filopodial protrusions. Our results showthat shape descriptors of synthetic cells resemble those of real cells, andthat our model is able to generate topologically plausible sequences of complexcell shapes in 3D+time.

Implicit Neural Representations for Generative Modeling of Living Cell Shapes