Fully automated condyle segmentation using 3D convolutional neural networks

Abstract The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were ac...

Full description

Bibliographic Details
Main Authors: Nayansi Jha, Taehun Kim, Sungwon Ham, Seung-Hak Baek, Sang-Jin Sung, Yoon-Ji Kim, Namkug Kim
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-24164-y