Sex estimation of the pelvis by deep learning of two-dimensional depth images generated from homologous models of three-dimensional computed tomography images

Sex estimation of the pelvis by deep learning of two-dimensional depth images generated from homologous models of three-dimensional computed tomography images

The utility of convolutional neural networks (CNNs) for sex estimation of the pelvis was evaluated using depth images generated from reconstructed three-dimensional (3D) computed tomography images. The 3D volume data were normalized by a homologous modeling technique to create polygon data with iden...

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Bibliographic Details
Main Authors: Mamiko Fukuta, Chiaki Kato, Hitoshi Biwasaka, Akihito Usui, Tetsuya Horita, Sanae Kanno, Hideaki Kato, Yasuhiro Aoki
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Forensic Science International: Reports
Subjects:
Forensic anthropology
Sex estimation
Pelvis
Deep learning
Homologous modeling
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2665910720300785

Internet

http://www.sciencedirect.com/science/article/pii/S2665910720300785

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