Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.
Convolutional neural network
Deep learning
Mask R-CNN
Pelvic tilt
Segmentation
Total hip arthroplasty
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Feb 2020
Feb 2020
Historique:
received:
08
04
2019
revised:
16
09
2019
accepted:
15
12
2019
pubmed:
3
1
2020
medline:
7
1
2021
entrez:
3
1
2020
Statut:
ppublish
Résumé
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 outperforms the U-Net. The cascaded system is capable of estimating the PSI with 4.04° ± 3.39° error for the radiography images. The proposed framework suggests a fully automatic and robust estimation of the PSI using only an anterior-posterior radiography image.
Sections du résumé
BACKGROUND AND OBJECTIVE
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
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
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
CONCLUSIONS
The proposed framework suggests a fully automatic and robust estimation of the PSI using only an anterior-posterior radiography image.
Identifiants
pubmed: 31896056
pii: S0169-2607(19)30502-4
doi: 10.1016/j.cmpb.2019.105282
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105282Informations de copyright
Copyright © 2019. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no conflict of interest.