DETECTION OF STANDARD PLANE FROM ULTRASOUND SCANS BY DEEP LEARNING METHODS FOR THE DIAGNOSIS OF DEVELOPMENTAL HIP DYSPLASIA

Yıl 2022, , 1014 - 1026, 30.09.2022

Kerim Kürşat Çevik , Şeyda Andaç

https://doi.org/10.21923/jesd.1064904

Öz

The term developmental dysplasia of the hip (DDH) describes a range of hip abnormalities affecting newborns where the femoral head and acetabulum are in improper alignment or grow abnormally, or both. The ultrasonographic evaluation technique rely on the capability of the ultrasonographer to pick up the accurate frame used for exact calculations. In our study we developed a new computer aided system that determines the exact frame from real time 2D ultrasound images and calculates the accuracy rate for each result. The deep learning architectures recently used in literature were utilized for these processes. In addition, transfer learning was carried out to increase the performance of the system using pretrained networks (SqueezeNet, VGG16, VGG19, ResNet50 and ResNet101). One of the best methods of object detection, You Only Look Once (YOLO) model, was used with pre-trained networks to determine DDH location. As a result of the study, the performance of the deep neural network model proposed with the help of these pre-trained networks was evaluated. When the obtained results were compared with expert opinions, frames (standard planes) in 605 of 676 (89.05%) test images were correctly detected. The accuracy rates for the used pre-trained networks were obtained as SqueezeNet 0.79, VGG16 0.95, VGG19 0.96, ResNet50 0.88 and ResNet101 0.93.

Anahtar Kelimeler

DDH, Standard Plane, Deep Learning, Transfer Learning, YOLO

GELİŞİMSEL KALÇA DİSPLAZİSİ TANISINDA DERİN ÖĞRENME YÖNTEMLERİYLE ULTRASON TARAMALARINDAN STANDART DÜZLEM TESPİTİ

Öz

Gelişimsel kalça displazisi (GKD) terimi, femur başı ve asetabulumun yanlış hizada olduğu, anormal şekilde büyüdüğü veya her ikisinin birden olduğu yeni doğanları etkileyen bir dizi kalça anormalliği olarak tanımlanır. Ultrasonografik değerlendirme tekniği, ultrasonografi uzmanının kesin hesaplamalar için kullanılan doğru çerçeveyi(standart düzlem) seçme yeteneğine dayanır. Çalışmamızda, gerçek zamanlı 2B ultrason görüntülerinden standart düzlemi belirleyen ve her bir sonuç için doğruluk oranını hesaplayan yeni bir bilgisayar destekli sistemi geliştirilmiştir. Bu işlemler için literatürde son zamanlarda kullanılan derin öğrenme mimarilerinden yararlanılmıştır. Ayrıca önceden eğitilmiş ağlar (SqueezeNet, VGG16, VGG19, ResNet50 ve ResNet101) kullanılarak, sistemin performansını artırmak için transfer öğrenmesi gerçekleştirilmiştir. Nesne algılamanın en iyi yöntemlerinden biri olan You Only Look Once (YOLO) modeli, DDH konumunu belirlemek için önceden eğitilmiş ağlarla birlikte kullanılmıştır. Çalışma sonucunda önceden eğitilmiş bu ağlar yardımıyla önerilen derin sinir ağı modelinin performansı değerlendirilmiştir. Elde edilen sonuçlar uzman görüşleri ile karşılaştırıldığında 676 test görüntüsünün 605(%89,05) 'inde doğru kareler (standart düzlemler) doğru olarak tespit edilmiştir. Kullanılan önceden eğitilmiş ağlar için doğruluk oranları SqueezeNet 0.79, VGG16 0.95, VGG19 0.96, ResNet50 0.88 ve ResNet101 0.93 olarak elde edilmiştir.

Anahtar Kelimeler

GKD, Standart Düzlem, Derin Öğrenme, Transfer Öğrenme, YOLO

Kaynakça

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