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https://doi.org/10.1109/tmi.2024.3398401
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Título: | Simulating the cellular context in synthetic datasets for cryo-electron tomography |
Fecha de publicación: | 8-may-2024 |
Editorial: | Institute of Electrical and Electronics Engineers |
Cita bibliográfica: | IEEE Transactions on Medical Imaging, Vol. XX, N. XX, XXXX 2023 |
ISSN: | Print: 0278-0062 Electronic: 1558-254X |
Palabras clave: | Cryo Electron Tomography Deep learning Image processing Scientific Computation Synthetic data generation |
Resumen: | 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. |
Autor/es principal/es: | Martinez-Sánchez, Antonio Lamm, Lorenz Jasnin, Marion Phelippeau, Harold |
Facultad/Departamentos/Servicios: | Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería de la Información y las Comunicaciones |
URI: | http://hdl.handle.net/10201/141860 |
DOI: | https://doi.org/10.1109/tmi.2024.3398401 |
Tipo de documento: | info:eu-repo/semantics/article |
Número páginas / Extensión: | 13 |
Derechos: | info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
Descripción: | © 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 |
Aparece en las colecciones: | Artículos: Ingeniería de la Información y las Comunicaciones |
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Fichero | Descripción | Tamaño | Formato | |
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TMI3398401_IEEE_accepted_version.pdf | Version aceptada | 34,53 MB | Adobe PDF | Visualizar/Abrir |
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