RT Journal Article
T1 Automated description of the mandible shape by deep learning
A1 Vila Blanco, Nicolás
A1 Varas Quintana, Paulina
A1 Aneiros Ardao, Ángela
A1 Tomás Carmona, Inmaculada
A1 Carreira Nouche, María José
K1 Convolutional neural networks
K1 Shape modeling
K1 Mandible morphometrics
K1 Deep learning
AB Purpose: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs).Methods: We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model.Results: The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectively
PB Springer
YR 2021
FD 2021
LK http://hdl.handle.net/10347/28995
UL http://hdl.handle.net/10347/28995
LA eng
NO International Journal of Computer Assisted Radiology and Surgery 16, 2215–2224 (2021). https://doi.org/10.1007/s11548-021-02474-2
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has received financial support from Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04 and Group with Growth Potential ED431B 2020-2022 GPC2020/27) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System
DS Minerva
RD 28 abr 2026