Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
}
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