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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00135215" target="_blank" >RIV/00216224:14330/24:00135215 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/24:10253054

Result on the web

<a href="https://doi.org/10.1016/j.media.2023.102991" target="_blank" >https://doi.org/10.1016/j.media.2023.102991</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.media.2023.102991" target="_blank" >10.1016/j.media.2023.102991</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2023050" target="_blank" >LM2023050: National Infrastructure for Biological and Medical Imaging</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Medical Image Analysis

ISSN

1361-8415

e-ISSN

1361-8423

Volume of the periodical

91

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

1-18

UT code for WoS article

001171218800001

EID of the result in the Scopus database

2-s2.0-85174029722