Diş Segmentasyonunda Segformer Yönteminin Model Parametreleri Üzerindeki Etkisinin Araştırılması

Abstract

Son yıllarda bilgisayar destekli tedavilerde yapay zekâ temelli uygulamalar diş hekimleri için hastalık teşhisinde kullanımı giderek artmaktadır. Diş hastalığın belirlenmesi sürecinde doğru bir şekilde bölütleme işlemi oldukça önemlidir. Diş bölütlenmesinin manuel olarak yapılması diş hekimleri tarafından yapılan teşhis için geçen süreyi ve işlem yükünü önemli ölçüde arttırmaktadır. Bu aşamada, makine öğrenmesi ve yapay zekâ yöntemleriyle otomatik olarak diş bölgesinin bölütlenmesi araştırmacıların ilgi duyduğu güncel bir konu olmaktadır. Çalışmada 12 diş stajyeri tarafından 15318 poligonlu 598 hastadan alınan X-Ray diş görüntüleri kullanılmaktadır. Kullanılan veri seti eğitim, doğrulama ve test olarak %70, %15, %15 olarak bölünmüştür. Bu veri seti otomatik olarak diş bölütlemeyi amaçlayan derin öğrenme ağının eğitim sürecinde kullanılmaktadır. SegFormer eğitim bloğu hiper parametrelerinin değişimine bağlı oluşturulan mimarilerinin performansları incelenmektedir. Burada MiT BO-B5 mimarilerine göre oluşturulan modellerin Dice benzerlik katsayılarına göre test verisi için performansları sırasıyla %92,61, %92,82, %93,25, %93,13, %93,17 ve %93,09 olarak elde edilmektedir. Elde edilen test sonuçlarına göre geliştirilen yapay zekâ tabanlı SegFormer ağı diş bölütlemeyi yüksek doğrulukla gerçekleştirmektedir. Geliştirilen derin öğrenme ağı özellikle diş hastalıklarının teşhisinde girdi olarak verimli bir şekilde kullanılabilecektir. Yüksek Dice benzerlik katsayıları, çalışmada sunulan SegFormer ağının diş bölgesini doğru bir şekilde tespit edebildiğini ifade etmektedir.

Keywords

• Yapay zekâ
• Diş bölütlenmesi
• SegFormer
• X-Ray görüntüleme
• Dice benzerlik katsayısı

Investigation of The Effect on Model Parameters in SegFormer Method for Tooth Segmentation

Abstract

In recent years, AI-based applications in computer-aided treatments have been increasingly used by dentists for disease diagnosis. Accurate segmentation is crucial in the process of identifying dental diseases. Manual segmentation of teeth significantly increases the time and workload for dentists when making diagnoses. At this stage, the automatic segmentation of the dental region using machine learning and artificial intelligence methods has become a topic of great interest for researchers. In the study, X-Ray tooth ratios taken from 598 patients with 15318 polygons by 12 dental interns are used. The dataset is split into 70% for training, 15% for validation, and 15% for testing. This dataset is used in the training process of a deep learning network for automatic tooth segmentation. The performances of architectures generated based on changes in the hyperparameters of the SegFormer training block are examined. Here, according to the Dice similarity coefficients of the models according to the Mix BO-B5 architectures, the performances for the test data are obtained as 92.61%, 92.82%, 93.25%, 93.13%, 93.17% and 93.09%, respectively. According to the test results obtained, the developed artificial intelligence-based SegFormer network performs tooth segmentation with high accuracy. The developed deep learning network can be efficiently used especially in the diagnosis of dental diseases. High Dice similarity coefficients indicate that the SegFormer network presented in this study can accurately detect the tooth region.

Keywords

• Artificial intelligence
• Tooth segmentation
• SegFormer
• X-Ray imaging
• Dice similarity coefficient

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