Simulating the cellular context in synthetic datasets for cryo-electron tomography

Martinez-Sanchez, Antonio; Lamm, Lorenz; Jasnin, Marion; Phelippeau, Harold

Publication:
Simulating the cellular context in synthetic datasets for cryo-electron tomography

dc.contributor.author	Martinez-Sanchez, Antonio
dc.contributor.author	Lamm, Lorenz
dc.contributor.author	Jasnin, Marion
dc.contributor.author	Phelippeau, Harold
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2024-05-27T08:33:28Z
dc.date.available	2024-05-27T08:33:28Z
dc.date.issued	2024-05-08
dc.description	© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in IEEE Transactions on Medical Imaging. To access the final edited and published work see https://doi.org/10.1109/tmi.2024.3398401
dc.description.abstract	Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.	es
dc.format	application/pdf	es
dc.format.extent	13
dc.identifier.citation	IEEE Transactions on Medical Imaging, vol. 43, no. 11, pp. 3742-3754, Nov. 2024
dc.identifier.doi	https://doi.org/10.1109/tmi.2024.3398401
dc.identifier.issn	Print: 0278-0062
dc.identifier.issn	Electronic: 1558-254X
dc.identifier.uri	http://hdl.handle.net/10201/141860
dc.language	eng	es
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	This work was supported by Ramon y Cajal program, Grant RYC2021-032626-I funded by MICIU/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR, of the Spanish State Research Agency (AEI) and Attract-RYC 2023 program of the University of Murcia. L.L. was funded from the Munich School for Data Science (MUDS) and a fellowship from the Boehringer Ingelheim Fonds.	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Cryo Electron Tomography
dc.subject	Deep learning
dc.subject	Image processing
dc.subject	Scientific Computation
dc.subject	Synthetic data generation
dc.title	Simulating the cellular context in synthetic datasets for cryo-electron tomography	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es