.png){kind=icon}
Return
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}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
deep learning
machine learning
CBCT
masseter muscle
orthodontic(s)
canio-maxillofacial surgery
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
5.1W
Citations:
9.8W
Organization
Cited Papers
ShareSaveProspectively-validated deep learning model for segmenting swallowing and chewing structures in CT
OAAI
OAAIShareSaveMasseter muscle changes following orthognathic surgery A long-term three-dimensional computed tomography follow-up.png)
ANGLE ORTHODONTISTOAAI
ANGLE ORTHODONTISTIF3.2
OAAIShareSaveU2-Net: Going deeper with nested U-structure for salient object detectionPATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSave
View more