Comparison of Transfer Learning Models in Pelvic Tilt and Rotation Measurement in Pediatric Anteroposterior Pelvic Radiographs

Abstract

The rotation and tilt of the pelvis during anteroposterior pelvic radiography can lead to misdiagnosis of developmental dysplasia of the hip (DDH) in children. At present, no method exists for accurately and conveniently measuring the precise rotation and tilt angles of pelvic on radiographs. The objective of this study was to develop several rotation and tilt measurement models using transfer learning and digital reconstructed radiographs (DRRs), and to compare their performances on pelvic radiographs. Based on the inclusion criteria, 30 of 92 children who underwent 3D hip CT scans at Xijing Hospital from 2015 to 2020 were included in the study. Using DRR techniques, radiographs were generated by rotating and tilting the pelvis in CT datasets at - 12 to 12° (projected every 3°) and were randomized to a 2:1:1 ratio of training dataset, validation dataset, and test dataset. Five pre-trained networks, including VGG16, Xception, VGG19, ResNet50 and InceptionV3 were used to develop pelvic rotation measurement models and tilt measurement models, and these models were trained with training dataset. The callback function was used during the training to slow down the learning rate when learning was stalled. Then, the validation set was used to optimize each model and compare their performances. At last, we tested the final performances of optimal rotation measurement model and optimal tilt measurement model on test dataset. The mean absolute error (MAE) was employed to assess the performance of the models. A total of 2430 pelvic DRRs were collected based on 30 CT datasets. Among 5 pre-trained transfer learning models, VGG16-Tilt achieved the best tilt prediction performance at the same BS and different LR. VGG16-Tilt model achieved its best performance on validation set at LR = 0.001 and BS = 4, and the final MAE on the test set was 0.5250°. In terms of rotation prediction, VGG16-Rotation also achieved the best performance, and it achieved its best performance on validation set at LR = 0.002 and BS = 8. The final MAE of VGG16-Rotation on the test set was 1.0731°. Pretrained transfer learning models worked well in predicting tilt and rotation angles of the pelvis on radiographs in children. Among them, VGG16-Tilt and VGG16-Rotation had the best effect in dealing with such problems despite their simple structures. These models deployed in devices can give orthopedic surgeons a powerful aid in DDH diagnosis.

Keywords: Convolutional neural network; Deep learning; Developmental dysplasia of the hip; Digital reconstructed radiograph; Misdiagnosis; Transfer learning.