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

• 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
• ISSN: 2665-9107

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 identical topology, then captured images for learning and testing. The neural networks were trained via transfer learning. As a result, a correct assignment rate >90% was obtained in most trials. The frontal view of the pelvis with 60-degree inclination achieved the best results. Selecting samples close to the average images of the sex was effective for training.