Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework

•Malposition of the acetabular component cause dislocation and prosthetic impingement after total hip arthroplasty, which significantly affect postoperative quality of life and implant longevity.•We introduce a new method for accurate estimation of functional PSI without requiring CT image in order to lower radiation exposure of the patient which opens up the possibility of increasing its application in a larger number of hospitals where CT is not acquired in a routine protocol.•We investigate the Mask R-CNN performance on radiography image segmentation and compare it with U-Net that widely is used in biomedical tasks.•The benefit of using transfer learning, multi-task learning and data augmentation is investigated. Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of the acetabular component is determined by the Pelvic Sagittal Inclination (PSI), which not only varies among different people but also changes in different positions. It is important to recognize individual dynamic changes of the PSI for patient-specific planning of the THA. Previously PSI was estimated by registering the CT and radiography images. In this study, we introduce a new method for accurate estimation of functional PSI without requiring CT image in order to lower radiation exposure of the patient which opens up the possibility of increasing its application in a larger number of hospitals where CT is not acquired as a routine protocol. The proposed method consists of two main steps: First, the Mask R-CNN framework was employed to segment the pelvic shape from the background in the radiography images. Then, following the segmentation network, another convolutional network regressed the PSI angle. We employed a transfer learning paradigm where the network weights were initialized by non-medical images followed by fine-tuning using radiography images. Furthermore, in the training process, augmented data was generated to improve the performance of both networks. We analyzed the role of segmentation network in our system and investigated the Mask R-CNN performance in comparison with the U-Net, which is commonly used for the medical image segmentation. In this study, the Mask R-CNN utilizing multi-task learning, transfer learning, and data augmentation techniques achieve 0.960 ± 0.008 DICE coefficient, which significantly