USCT: Uncertainty-regularized symmetric consistency learning for semi-supervised teeth segmentation in CBCT
Authors:
- Yixin JingYJYixin Jing
- Jie LiuJLJie Liu
- Weifan LiuWLWeifan Liu
- Zhicheng YangZYZhicheng Yang
- ZhongWei ZhouZZZhongWei Zhou
- Zekuan YuZYZekuan Yu
- 6 February 2024
- journal article
- research article
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 91
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
- Fudan University
This publication has 32 references indexed in Scilit:
- Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks2018
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation2015
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)IEEE Transactions on Medical Imaging, 2015
- Toward accurate tooth segmentation from computed tomography images using a hybrid level set modelMedical Physics, 2015
- Artefacts in CBCT: a reviewDentomaxillofacial Radiology, 2011
- Individual tooth segmentation from CT images using level set method with shape and intensity priorPattern Recognition, 2010
- Modern dental imaging: a review of the current technology and clinical applications in dental practiceEuropean Radiology, 2010
- Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: A systematic review of the literatureInternational Journal of Oral and Maxillofacial Surgery, 2009