Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set.

Journal: International journal of legal medicine

Published Date: Jan 1, 2021

Abstract

OBJECTIVES: To develop an automatic segmentation method to segment the pulp chamber of first molars from 3D cone-beam-computed tomography (CBCT) images, and to estimate ages by calculated pulp volumes.

Authors

Qiang Zheng

First People's Hospital of Zunyi City, Zunyi, China.
Zhipu Ge

Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, China.
Han Du

Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, China.
Gang Li

The Centre for Cyber Resilience and Trust, Deakin University, Australia.

Keywords

Adolescent Adult Age Determination by Teeth Aged Child Cone-Beam Computed Tomography Deep Learning Dental Pulp Cavity Female Humans Imaging, Three-Dimensional Linear Models Male Middle Aged Molar Retrospective Studies Young Adult

External Resources

View on PubMed Access via DOI PubMed (33185706)

Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set.

Abstract

Authors

Keywords

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