Barış Oğuz GÜRSES, Aysun BALTACI, Uğur TEKİN, Pelin GÜNERİ, Figen GOKMEN, Elif ŞENER, Rukiye Irmak TURHAL, Hayal BOYACIOĞLU, Sercan SABANCI

Manuel Segmentasyon, Diş Segmentasyonu için Gerçek Altın Standart mı? Konik Işınlı Bilgisayarlı Tomografi Görüntüleri Üzerinde Bir Ön in vivo Çalışma

Amaç: Manuel segmentasyonun (bölütleme) diş hacmini ölçmek için doğru bir yöntem olup olmadığını değerlendirmek ve konik ışınlı bilgisayarlı tomografi (CBCT) görüntülerinde manuel, otomatik ve yarı otomatik segmentasyon sonuçlarının her birini altın standart olarak kabul edilen su deplasman yöntemi ile karşılaştırmaktır. Gereç ve Yöntemler: Bu ön in vivo çalışmada maksiller gömülü 10 dişin CBCT görüntüleri kullanılmıştır. CBCT taramalarının alınmasının ardından manuel, otomatik ve yarı otomatik segmentasyon aynı operatör tarafından tamamlanmıştır. Cerrahi olarak çıkarıldıktan sonra tüm gömülü dişlerin hacimleri altın standart olarak kullanılan su deplasman yöntemi ile ölçülmüştür. Her bölütlenmiş görüntünün hacmi, 3D-Doctor yazılımı kullanılarak mm3cinsinden ölçülmüştür. Tüm bölütlenmiş görüntülerin belirlenen hacimleri, %95 güven aralığı bootstrap yüzdelikleri kullanılarak altın standartlarla karşılaştırılmıştır. Gözlemci içi güvenilirlik, sınıf içi korelasyon katsayısı kullanılarak belirlenmiştir. Bulgular: Tüm segmentasyon yöntemleri hem altın standarttan hem de birbirinden önemli ölçüde farklı hacim değerleri ortaya çıkarmıştır (p=0,000). Yarı otomatik bölütleme, manuel yönteme benzer performans ortaya koymuş ve her iki sistem de otomatik yönteme göre altın standartla karşılaştırılabilir hacimler sağlamıştır. Tüm protokoller için mükemmel gözlemci içi sınıf içi korelasyonlar bulunmuştur. Sonuç: Manuel, yarı otomatik ve otomatik segmentasyonla elde edilen sonuçlar numunelerin gerçek hacimlerinden farklı bulunmuştur. Yarı otomatik segmentasyon, manuel yönteme benzer performans sağlarken, otomatik segmentasyon altın standarda en uzak sonuçları sunmuştur. Otomatik ve yarı otomatik bölütleme, daha hızlı işlem ve daha doğru sonuçlar için yeni veya hibrit bölümleme algoritmalarının geliştirilmesi ve kullanılmasıyla iyileştirilebilir.

Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary in vivo Study Using Conebeam Computed Tomography Images

Objective: This study aimed to assess whether manual segmentation is an accurate method in tooth volume measurement and to compare the outcomes of manual, automatic, and semiautomatic segmentations on cone-beam computed tomography (CBCT) images by comparing each system with the water displacement method, which is the gold standard. Materials and Methods: CBCT images of l0 maxillary impacted teeth were used in this preliminary in vivo study. Following the acquisition of CBCT scans, manual, automatic, and semiautomatic segmentations were completed by the same operator. After surgical removal, the volumes of all impacted teeth were measured with the water displacement method, which was used as the gold standard. The volume of each segmented image was measured in mm3using the 3D-Doctor software. The established volumes of each segmented image were compared with those of the gold standard using the 95% confidence interval bootstrap percentiles. Intraobserver reliability was determined using the intraclass correlation coefficient. Results: All segmentation methods revealed significantly different volume values both from the gold standard and from each other (p=0.000). The semiautomatic segmentation demonstrated comparable performance with the manual method, and both systems provided comparable volumes with the gold standard than did the automatic method. Excellent intra-observer intraclass correlations were found for all protocols. Conclusion: The actual volumes of the specimen were not obtained by manual, semiautomatic, and automatic segmentations. Semiautomatic segmentation demonstrated comparable performance to the manual method, whereas automatic segmentation yielded the poorest values. The automatic and semiautomatic segmentations may be improved by the development and utilization of novel or hybrid segmentation algorithms for a faster process and more accurate results.

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