Büyükten Küçüğe Oto-kodlayıcılar ile dişlerin konumlandırılması*

Dişlerin lokalizasyonu, bilgisayar destekli gerçekleştirilen dental görüntülerden insan kimliklendirme ve medikal tanı için bir önşarttır. Konvolusyonel sinir ağları ve oto-kodlayıcılar gibi klasik derin öğrenme mimarileri diş tanıma işlemi için başarılı gözükse de dental görüntülerin çok büyük olması nedeniyle tüm arama uzayının taranması mümkün gözükmemektedir. Bu çalışmada, büyükten-küçüğe yığınlanmış bir oto-kodlayıcı yapısı ile dental görüntülerden dişleri tanıyan bir sistem sunulmuştur. Önerilen mimari, girdi görüntü yamalarının boyutlarının her kademede arttığı bir kademeli yığınlanmış oto-kodlayıcı yapısı içerir. İlerdeki katmanlara sadece bulunan aday diş yamaları verilir; böylece alakasız yamalar elimine edilmiş olur. Önerilen mimari tanıma aşamasındaki maliyeti düşürmekle beraber hassas konumlandırma imkanı sunar. Geliştirilen metot, 206 dental panoramik görüntü içeren bir veri kümesi üzerinde test edilmiştir ve sonuçlar ümit vericidir.

Tooth Localization with Coarse-to-Fine Auto-Encoders

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 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 increase 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 206 dental panoramic images and the results are promising.

Keywords:

Coarse-to-Fine auto-encoder, detection deep learning,

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