.png){kind=icon}
返回
Automatic Masseter Muscle Accurate Segmentation from CBCT Using Deep Learning-Based Model
.png){kind=icon}
OA
AI
DOI：10.3390/jcm12010055.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Segmentation of the masseter muscle (MM) on cone-beam computed tomography (CBCT) is challenging due to the lack of sufficient soft-tissue contrast. Moreover, manual segmentation is laborious and time-consuming. The purpose of this study was to propose a deep learning-based automatic approach to accurately segment the MM from CBCT under the refinement of high-quality paired computed tomography (CT). Fifty independent CBCT and 42 clinically hard-to-obtain paired CBCT and CT were manually annotated by two observers. A 3D U-shape network was carefully designed to segment the MM effectively. Manual annotations on CT were set as the ground truth. Additionally, an extra five CT and five CBCT auto-segmentation results were revised by one oral and maxillofacial anatomy expert to evaluate their clinical suitability. CBCT auto-segmentation results were comparable to the CT counterparts and significantly improved the similarity with the ground truth compared with manual annotations on CBCT. The automatic approach was more than 332 times shorter than that of a human operation. Only 0.52% of the manual revision fraction was required. This automatic model could simultaneously and accurately segment the MM structures on CBCT and CT, which can improve clinical efficiency and efficacy, and provide critical information for personalized treatment and long-term follow-up.
Keyword:
deep learning
machine learning
CBCT
masseter muscle
orthodontic(s)
canio-maxillofacial surgery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
5.1W
被引数:
9.8W
机构
引用论文
Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT
OAAI
OAAI分享收藏Analysis of the formation of monodisperse populations by homogeneous nucleation通过均相成核分析单分散种群的形成
PREAI
分享收藏Masseter muscle changes following orthognathic surgery A long-term three-dimensional computed tomography follow-up.png)
ANGLE ORTHODONTISTOAAI
ANGLE ORTHODONTISTIF3.2
OAAI分享收藏U2-Net: Going deeper with nested U-structure for salient object detectionU2-Net: 基于嵌套U结构的显著性目标检测PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Application of exponential functions in weighted residuals method in structural mechanics. Part II: static and vibration analysis of rectangular plate结构力学中加权残差法中指数函数的应用。第二部分：矩形板的静力与振动分析OAAI
OAAI分享收藏
查看更多内容