Estimation of Pelvic Sagital Inclanation from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study

Ata Jodeiri; Yoshito Otake; Reza A. Zoroofi; Yuta Hiasa; Masaki Takao; Keisuke Uemura; Nobuhiko Sugano; Yoshinobu Sato

doi:10.29007/w6t7

Estimation of Pelvic Sagital Inclanation from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study

5 pages•Published: July 12, 2018

Ata Jodeiri, Yoshito Otake, Reza A. Zoroofi, Yuta Hiasa, Masaki Takao, Keisuke Uemura, Nobuhiko Sugano and Yoshinobu Sato

Abstract

Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standing position is an important factor in planning of cup alignment [1] and has been estimated mainly from radiographs. Previous methods for PSI estimation [2], [3] used a patient-specific CT to create digitally reconstructed radiographs (DRRs) and compare them with the radiograph to estimate relative position between the pelvis and the x-ray detector. In this study, we developed a method that estimates PSI angle from a single anteroposterior radiograph using two convolutional neural networks (CNNs) without requiring the patient-specific CT, which reduces radiation exposure of the patient and opens up the possibility of application in a larger number of hospitals where CT is not acquired in a routine protocol.

Keyphrases: Convolutional Neural Network, Pelvic sagittal inclination, Radiograph

In: Wei Tian and Ferdinando Rodriguez Y Baena (editors). CAOS 2018. The 18th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 2, pages 114--118

Links:	https://easychair.org/publications/paper/Hrzk
	https://doi.org/10.29007/w6t7

BibTeX entry

@inproceedings{CAOS2018:Estimation_of_Pelvic_Sagital,
  author    = {Ata Jodeiri and Yoshito Otake and Reza A. Zoroofi and Yuta Hiasa and Masaki Takao and Keisuke Uemura and Nobuhiko Sugano and Yoshinobu Sato},
  title     = {Estimation of Pelvic Sagital Inclanation from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study},
  booktitle = {CAOS 2018. The 18th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Wei Tian and Ferdinando Rodriguez Y Baena},
  series    = {EPiC Series in Health Sciences},
  volume    = {2},
  pages     = {114--118},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/Hrzk},
  doi       = {10.29007/w6t7}}

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