scholarly journals 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning‐based CBCT image auto‐segmentation

2021 ◽  
Author(s):  
Xiaoyu Wang ◽  
Matthew Pastewait ◽  
Tai‐Hsien Wu ◽  
Chunfeng Lian ◽  
Beatriz Tejera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Cbct Image

scholarly journals Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Yoshitaka Kise ◽  
Takuma Funakoshi ◽  
Motoki Fukuda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Panoramic Radiography ◽  
False Positive Rate ◽  
Normal Subjects ◽  
Learning System ◽  
Panoramic Radiographs ◽  
Positive Rate ◽  
Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

Refined tooth and pulp segmentation using U-Net in CBCT image

2021 ◽  
pp. 20200251
Author(s):  
Wei Duan ◽  
Yufei Chen ◽  
Qi Zhang ◽  
Xiang Lin ◽  
Xiaoyu Yang
Keyword(s):  
Deep Learning ◽  
Dental Pulp ◽  
Small Data ◽  
Two Phase ◽  
Valuable Data ◽  
Pulp Cavity ◽  
Structural Problems ◽  
Single Tooth ◽  
Cbct Image ◽  
Feature Pyramid

Objectives The aim of this study was extracting any single tooth from a CBCT scan and performing tooth and pulp cavity segmentation to visualize and to have knowledge of internal anatomy relationships before undertaking endodontic therapy. Methods: We propose a two-phase deep learning solution for accurate tooth and pulp cavity segmentation. First, the single tooth bounding box is extracted automatically for both single-rooted tooth (ST) and multirooted tooth (MT). It is achieved by using the Region Proposal Network (RPN) with Feature Pyramid Network (FPN) method from the perspective of panorama. Second, U-Net model is iteratively performed for refined tooth and pulp segmentation against two types of tooth ST and MT, respectively. In light of rough data and annotation problems for dental pulp, we design a loss function with a smoothness penalty in the network. Furthermore, the multi-view data enhancement is proposed to solve the small data challenge and morphology structural problems. Results: The experimental results show that the proposed method can obtain an average dice 95.7% for ST, 96.2% for MT and 88.6% for pulp of ST, 87.6% for pulp of MT. Conclusions This study proposed a two-phase deep learning solution for fast and accurately extracting any single tooth from a CBCT scan and performing accurate tooth and pulp cavity segmentation. The 3D reconstruction results can completely show the morphology of teeth and pulps, it also provides valuable data for further research and clinical practice.

scholarly journals Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy

2020 ◽  
pp. 229-229 ◽  
Author(s):  
Sven Kuckertz ◽  
Nils Papenberg ◽  
Jonas Honegger ◽  
Tomasz Morgas ◽  
Benjamin Haas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Radio Therapy ◽  
Cbct Image

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

2021 ◽  
Author(s):  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Tsutomu Kuwada ◽  
Kenichi Gotoh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Panoramic Radiography ◽  
False Positive Rate ◽  
Normal Subjects ◽  
Learning System ◽  
Panoramic Radiographs ◽  
Positive Rate ◽  
Aided Diagnosis

Abstract Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of the present study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP group) or without cleft palate (CA only group) and 210 patients without CA (normal group) were used to create 2 learning models on the DetectNet. The models 1 and 2 were developed based on the data with and without normal subjects, respectively, to detect the CAs and classify them into the CA only and CA with CP groups. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The model 2 performances were higher in almost values than those in the model 1, but no difference in the recall of CA with CP groups. The model created in the present study appeared to have the potential to detect and classify CAs on panoramic radiographs.

Deep learning based CT-CBCT image registration for adaptive radio therapy

2020 ◽  
Author(s):  
Sven Kuckertz ◽  
Nils Papenberg ◽  
Jonas Honegger ◽  
Tomasz Morgas ◽  
Benjamin Haas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Radio Therapy ◽  
Cbct Image

Deep learning-aided CBCT image reconstruction of interventional material from four x-ray projections

2020 ◽  
Author(s):  
Elias Eulig ◽  
Joscha Maier ◽  
N. Robert Bennett ◽  
Michael Knaup ◽  
Klaus Hörndler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
X Ray ◽  
Cbct Image

scholarly journals Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yangdong Lin ◽  
Miao He
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Surgical Planning ◽  
Three Dimensional ◽  
Cone Beam ◽  
Facial Surgery ◽  
Cbct Image ◽  
Dimensional Cone ◽  
Computer Based ◽  
Lesion Localization

In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.

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