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Computational anatomy atlas using multilayer perceptron with Lipschitz regularization. / Ushenin, Konstantin; Dordiuk, Vladislav; Dzhigil, Maksim.
SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. стр. 680-683 (SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

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Harvard

Ushenin, K, Dordiuk, V & Dzhigil, M 2022, Computational anatomy atlas using multilayer perceptron with Lipschitz regularization. в SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings, Institute of Electrical and Electronics Engineers Inc., стр. 680-683, 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 11/11/2022. https://doi.org/10.1109/SIBIRCON56155.2022.10016940

APA

Ushenin, K., Dordiuk, V., & Dzhigil, M. (2022). Computational anatomy atlas using multilayer perceptron with Lipschitz regularization. в SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (стр. 680-683). (SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON56155.2022.10016940

Vancouver

Ushenin K, Dordiuk V, Dzhigil M. Computational anatomy atlas using multilayer perceptron with Lipschitz regularization. в SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. стр. 680-683. (SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). doi: 10.1109/SIBIRCON56155.2022.10016940

Author

Ushenin, Konstantin ; Dordiuk, Vladislav ; Dzhigil, Maksim. / Computational anatomy atlas using multilayer perceptron with Lipschitz regularization. SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. стр. 680-683 (SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

BibTeX

@inproceedings{ba2bfc3490884ae3aee41cb636436096,
title = "Computational anatomy atlas using multilayer perceptron with Lipschitz regularization",
abstract = "A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, such as voxel models or surface meshes. In this paper, we propose a method of atlas generation using an implicit representation of 3D objects. Our approach has two key stages. The first stage converts voxel models of segmented organs to implicit form using the usual multilayer perceptron. This stage smooths the model and reduces memory consumption. The second stage uses a multilayer perceptron with Lipschitz regularization. This neural network provides a smooth transition between implicitly defined 3D geometries. Our work shows examples of models of the left and right human ventricles. All code and data for this work are open. {\textcopyright} 2022 IEEE.",
author = "Konstantin Ushenin and Vladislav Dordiuk and Maksim Dzhigil",
year = "2022",
doi = "10.1109/SIBIRCON56155.2022.10016940",
language = "English",
series = "SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "680--683",
booktitle = "SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
address = "United States",
note = "2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) ; Conference date: 11-11-2022 Through 13-11-2022",

}

RIS

TY - GEN

T1 - Computational anatomy atlas using multilayer perceptron with Lipschitz regularization

AU - Ushenin, Konstantin

AU - Dordiuk, Vladislav

AU - Dzhigil, Maksim

PY - 2022

Y1 - 2022

N2 - A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, such as voxel models or surface meshes. In this paper, we propose a method of atlas generation using an implicit representation of 3D objects. Our approach has two key stages. The first stage converts voxel models of segmented organs to implicit form using the usual multilayer perceptron. This stage smooths the model and reduces memory consumption. The second stage uses a multilayer perceptron with Lipschitz regularization. This neural network provides a smooth transition between implicitly defined 3D geometries. Our work shows examples of models of the left and right human ventricles. All code and data for this work are open. © 2022 IEEE.

AB - A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, such as voxel models or surface meshes. In this paper, we propose a method of atlas generation using an implicit representation of 3D objects. Our approach has two key stages. The first stage converts voxel models of segmented organs to implicit form using the usual multilayer perceptron. This stage smooths the model and reduces memory consumption. The second stage uses a multilayer perceptron with Lipschitz regularization. This neural network provides a smooth transition between implicitly defined 3D geometries. Our work shows examples of models of the left and right human ventricles. All code and data for this work are open. © 2022 IEEE.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85147528131

U2 - 10.1109/SIBIRCON56155.2022.10016940

DO - 10.1109/SIBIRCON56155.2022.10016940

M3 - Conference contribution

T3 - SIBIRCON - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

SP - 680

EP - 683

BT - SIBIRCON 2022 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)

Y2 - 11 November 2022 through 13 November 2022

ER -

ID: 34717581