U-Net ve Morfolojik İşlemler Kullanılarak Panoramik Radyografiler Üzerinde Diş Örneği Bölütleme

Year 2022, , 39 - 50, 31.01.2022

Serdar Helli , Andaç Hamamcı

https://doi.org/10.29130/dubited.950568

Cited By: 4

Abstract

Panoramik röntgen görüntülerinde otomatik diş bölütleme, diş hekimliği görüntü analizinin önemli bir araştırma konusudur. Bu çalışmada, görüntüdeki nesnelerin ayrıldığı bir bölütleme haritası elde etmek için bir son işleme aşaması öneriyoruz ve bu tekniği U-Net ağı ile diş örneği bölütlemeye uyguluyoruz. Son işleme, ikilileştirmeden önce ağın sigmoid çıkışına uygulanan gri tonlamalı morfolojik ve filtreleme işlemlerinden oluşmaktadır. Tüm diş bölütlemede %95,4±0,3'lük bir Dice örtüşme puanı elde edilmiştir. Önerilen son işleme aşamaları, diş sayısının tespitinde ortalama hatayı %26,81'den %6,15'e düşürmüştür. Bildiğimiz kadarıyla hem bölütleme, hem de diş sayma performansları literatürdeki en yüksek performanslardır. Ayrıca bu sonuç, 105 görüntüden oluşan nispeten küçük bir eğitim veri seti kullanılarak elde edilmiştir. Bu çalışmadaki amaç diş örneklerini bölütlemek olsa da, sunulan yöntem hücre örneklerini ayırmak gibi diğer alanlardaki benzer problemlere uygulanabilir. 

Keywords

Diş, Panoramik, Bölütleme, Örnek bölütleme, Sayma, U-Net

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

Abstract

Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4±0.3% is obtained in overall teeth segmentation. The proposed post-processing stages reduce the mean error of tooth count to 6.15%, whereas the error without post-processing is 26.81%. The performances of both segmentation and tooth counting are the highest in the literature, to our knowledge. Moreover, this is achieved by using a relatively small training dataset, which consists of 105 images. Although the aim in this study is to segment tooth instances, the presented method is applicable to similar problems in other domains, such as separating the cell instances. 

Keywords

Dental, Panoramic, Segmentation, Instance Segmentation, Counting, U-net

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