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Concurrent Learning Approach for Estimation of Pelvic Tilt From Anterior–Posterior Radiograph Publisher



Jodeiri A1, 2 ; Seyedarabi H1 ; Danishvar S3 ; Shafiei SH4 ; Sales JG5 ; Khoori M6 ; Rahimi S4 ; Mortazavi SMJ6
Authors
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Authors Affiliations
  1. 1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 51666, Iran
  2. 2. Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, 51656, Iran
  3. 3. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom
  4. 4. Orthopedic Surgery Research Centre, Sina University Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, 51656, Iran
  5. 5. Department of Orthopedic Surgery, Shohada Hospital, Tabriz University of Medical Sciences, Tabriz, 51656, Iran
  6. 6. Joint Reconstruction Research Center (JRRC), Tehran University of Medical Sciences, Tehran, 51656, Iran

Source: Bioengineering Published:2024


Abstract

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior–posterior (AP) radiography image. We introduce an encoder–decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks. © 2024 by the authors.