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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Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.