Automatic segmentation of mandible in panoramic x-ray

Abdi, Ali A.; Kasaei, Shohreh; Mehdizadeh, Mehrzad

doi:10.1117/1.jmi.2.4.044003

Cited by 62 publications

(60 citation statements)

References 21 publications

(29 reference statements)

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“…Conventional approaches for mandible segmentation typically use pixel-based or model-based methods. In the past decade, some traditional semi-automatic and automatic methods have been developed to segment the mandible in CT scans (Gollmer & Buzug 2012) (Torosdagli et al 2017) (Chuang, Doherty, Adluru, Chung & Vorperian 2017) (Abdi, Kasaei & Mehdizadeh 2015) )(Mannion-Haworth, Bowes, Ashman, Guillard, Brett & Vincent 2015 . A statistical shape model for mandible segmentation was presented by Gollmer and Buzug in 2012 (Gollmer & Buzug 2012).…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

et al. 2019

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“…Such computer-assisted segmentation methods help reduce the time needed to create precise and accurate 3D mandible models, and can be particularly beneficial for large-scale studies. These methods have been devised for application in dental surgical/treatment planning 1,2 , orthodontic diagnoses 3 , and dental forensics 4 to reconstruct three dimensional (3D) mandible models from imaging studies such as panoramic x-rays, cone-beam computed tomography (CBCT), and multi-detector computed tomography (MDCT).…”

Section: Introductionmentioning

confidence: 99%

A Novel Registration-Based Semiautomatic Mandible Segmentation Pipeline Using Computed Tomography Images to Study Mandibular Development

Chuang¹,

Doherty²,

Adluru³

et al. 2018

J Comput Assist Tomogr

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“…The dataset used in [9] has been used as the starting point. This dataset contains 116 volumes of panoramic dental radiographs.…”

Section: Materials and Methodologymentioning

confidence: 99%

Dataset Augmentation for Machine Learning Applications of Dental Radiography

Khan¹,

Mukati²

2020

IJACSA

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The performance of any machine learning algorithm heavily depends on the quality and quantity of the training data. Machine learning algorithms, driven by training data can accurately predict and produce the right outcome when trained through enough amount of quality data. In the medical applications, being more critical, the accuracy is of utmost importance. Obtaining medical imaging data, enough to train machine learning algorithm is difficult due to a variety of reasons. An effort has been made to produce an augmented dental radiography dataset to train machine learning algorithms. 116 panoramic dental radiographs have been manually segmented for each tooth producing 32 classes of teeth. Out of 3712 images of individual tooth, 2910 were used for machine learning through general augmentation methods that include rotation, intensity transformation and flipping of the images, creating a massive dataset of 5.12 million unique images. The dataset is labeled and classified into 32 classes. This dataset can be used to train deep convolutional neural networks to perform classification and segmentation of teeth in x-rays, Cone-Beam CT scans and other radiographs. We retrained AlexNet on a subset of 80,000 images of the entire dataset and obtained classification accuracy of 98.88% on 10 classes. The retraining on original dataset yielded 88.31%. The result is evident of nearly a 10% increase in the performance of the classifier trained on the augmented dataset. The training and validation datasets include teeth affected with metal objects. The manually segmented dataset can be used as a benchmark to evaluate the performance of machine learning algorithms for performing tooth segmentation and tooth classification.

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Automatic segmentation of mandible in panoramic x-ray

Cited by 62 publications

References 21 publications

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

A Novel Registration-Based Semiautomatic Mandible Segmentation Pipeline Using Computed Tomography Images to Study Mandibular Development

Dataset Augmentation for Machine Learning Applications of Dental Radiography

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