Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets

Abstract The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. W...

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Bibliographic Details

Published in:	BMC Oral Health
Main Authors:	Su Yang, Jong Soo Jeong, Dahyun Song, Ji Yong Han, Sang-Heon Lim, Sujeong Kim, Ji-Yong Yoo, Jun-Min Kim, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Won-Jin Yi
Format:	Article
Language:	English
Published:	BMC 2025-07-01
Subjects:	Deep learning CBCT image Mandibular canal Image segmentation 3D segmentation network
Online Access:	https://doi.org/10.1186/s12903-025-06483-4