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Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep
Ata Jodeiri1, Reza A Zoroofi2, Yuta Hiasa3
1School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, North Kargar st., Tehran 1439957131, Iran.; Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
Background And Objective:
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.
Methods:
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.
Results:
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 outperforms the U-Net. The cascaded system is capable of estimating the PSI with 4.04° ± 3.39° error for the radiography images.
Conclusions:
The proposed framework suggests a fully automatic and robust estimation of the PSI using only an anterior-posterior radiography image.