Büyükten Küçüğe Oto-kodlayıcılar ile dişlerin konumlandırılması*

Abstract

Localization of teeth is a prerequisite task for most of the computerized methods for dental images such as medical diagnosis and human identification. Classical deep learning architectures like convolutional neural networks and auto-encoders seem to work well for tooth detection, however, it is non-trivial because of the large dental image size. In this study, a coarse-to-fine stacked auto-encoder architecture is presented for detection of teeth in dental panoramic images. The proposed architecture involves cascaded stacked auto-encoders where sizes of the input patches are increased with the successive steps. Only the detected candidate tooth patches are fed into the successive layers, thus the irrelevant patches are eliminated. The proposed architecture decreases the cost of detection process while providing precise localization. The method is tested and validated on a dataset containing 210 dental panoramic images and the tooth detection accuracy of the system is 91%.

Convolutional Neural Networks in Orthodontics: a review

“…Classification Oktay [99] applies a AlexNet-inspired architecture to classify between 3 types of teeth and the background. Next, the same author published a paper [100] which concerns placing a landmark on each tooth. For this, they developed a stacked auto-encoder to narrow down on a ROI to maintain a high resolution.…”