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...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-11-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-24164-y |