Fully automated condyle segmentation using 3D convolutional neural networks

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...

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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

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