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Proceedings Paper

Automatic pedicles detection using convolutional neural network in a 3D spine reconstruction from biplanar radiographs
Author(s): Christine Bakhous; Benjamin Aubert; Carlos Vazquez; Thierry Cresson; Stefan Parent; Jacques De Guise
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Paper Abstract

The 3D analysis of the spine deformities (scoliosis) has a high potential in its clinical diagnosis and treatment. In a biplanar radiographs context, a 3D analysis requires a 3D reconstruction from a pair of 2D X-rays. Whether being fully-/semiautomatic or manual, this task is complex because of the noise, the structure superimposition and partial information due to a limited projections number. Being involved in the axial vertebra rotation (AVR), which is a fundamental clinical parameter for scoliosis diagnosis, pedicles are important landmarks for the 3D spine modeling and pre-operative planning. In this paper, we focus on the extension of a fully-automatic 3D spine reconstruction method where the Vertebral Body Centers (VBCs) are automatically detected using Convolutional Neural Network (CNN) and then regularized using a Statistical Shape Model (SSM) framework. In this global process, pedicles are inferred statistically during the SSM regularization. Our contribution is to add a CNN-based regression model for pedicle detection allowing a better pedicle localization and improving the clinical parameters estimation (e.g. AVR, Cobb angle). Having 476 datasets including healthy patients and Adolescent Idiopathic Scoliosis (AIS) cases with different scoliosis grades (Cobb angles up to 116°), we used 380 for training, 48 for testing and 48 for validation. Adding the local CNN-based pedicle detection decreases the mean absolute error of the AVR by 10%. The 3D mean Euclidian distance error between detected pedicles and ground truth decreases by 17% and the maximum error by 19%. Moreover, a general improvement is observed in the 3D spine reconstruction and reflected in lower errors on the Cobb angle estimation.

Paper Details

Date Published: 27 February 2018
PDF: 9 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750L (27 February 2018); doi: 10.1117/12.2293763
Show Author Affiliations
Christine Bakhous, École de Technologie Supérieure (Canada)
Benjamin Aubert, École de Technologie Supérieure (Canada)
Carlos Vazquez, École de Technologie Supérieure (Canada)
Thierry Cresson, École de Technologie Supérieure (Canada)
Stefan Parent, Sainte-Justine Hospital Research Ctr. (Canada)
Jacques De Guise, École de Technologie Supérieure (Canada)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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