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A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study

Authors :
Guoyan Zheng
Cheng Chen
Steffen Schumann
Jochen Franke
Weiguo Xie
Lutz-Peter Nolte
Paul Alfred Grützner
Source :
The International Journal of Medical Robotics and Computer Assisted Surgery. 11:166-180
Publication Year :
2014
Publisher :
Wiley, 2014.

Abstract

Background Complete-pelvis segmentation in antero-posterior pelvic radiographs is required to create a patient-specific three-dimensional pelvis model for surgical planning and postoperative assessment in image-free navigation of total hip arthroplasty. Methods A fast and robust framework for accurately segmenting the complete pelvis is presented, consisting of two consecutive modules. In the first module, a three-stage method was developed to delineate the left hemipelvis based on statistical appearance and shape models. To handle complex pelvic structures, anatomy-specific information processing techniques were employed. As the input to the second module, the delineated left hemi-pelvis was then reflected about an estimated symmetry line of the radiograph to initialize the right hemi-pelvis segmentation. The right hemi-pelvis was segmented by the same three-stage method, Results Two experiments conducted on respectively 143 and 40 AP radiographs demonstrated a mean segmentation accuracy of 1.61±0.68 mm. A clinical study to investigate the postoperative assessment of acetabular cup orientations based on the proposed framework revealed an average accuracy of 1.2°±0.9° and 1.6°±1.4° for anteversion and inclination, respectively. Delineation of each radiograph costs less than one minute. Conclusions Despite further validation needed, the preliminary results implied the underlying clinical applicability of the proposed framework for image-free THA.

Details

ISSN :
14785951
Volume :
11
Database :
OpenAIRE
Journal :
The International Journal of Medical Robotics and Computer Assisted Surgery
Accession number :
edsair.doi...........94e238690958d501732466f59f7c9fc8