MRCM‐UCTransNet: Automatic and Accurate 3D Tooth Segmentation Network From Cone‐Beam CT Images

ABSTRACT Many scenarios in dental clinical diagnosis and treatment require the segmentation and identification of a specific tooth or the entire dentition in cone‐beam computed tomography (CBCT) images. However, traditional segmentation methods struggle to ensure accuracy. In recent years, there has...

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Veröffentlicht in:	International journal of imaging systems and technology Jg. 34; H. 4
Hauptverfasser:	Wen, Xinyang, Liu, Zhuoxuan, Chu, Yanbo, Le, Min, Li, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.07.2024 Wiley Subscription Services, Inc
Schlagworte:	Accuracy Algorithms attention mechanism Computed tomography cone‐beam CT Effectiveness Image enhancement Image segmentation Machine learning Medical imaging multi‐scale residual convolution Parameter sensitivity Teeth tooth segmentation UCTransNet
ISSN:	0899-9457, 1098-1098
Online-Zugang:	Volltext
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