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Automatic CNN-based 3D/2D non-rigid registration platform for fast 3D femur reconstruction and clinical 3D measurements from Bi-planar radiographs

Khameneh, Nahid Babazadeh, Cresson, Thierry, Lavoie, Frédéric, de Guise, Jacques and Vázquez, Carlos. 2025. « Automatic CNN-based 3D/2D non-rigid registration platform for fast 3D femur reconstruction and clinical 3D measurements from Bi-planar radiographs ». Computers in Biology and Medicine, vol. 196, nº Part A.

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Abstract

Purpose: This paper presents an automatic 3D/2D non-rigid registration method for fast 3D reconstruction and clinical measurements of the femur. Approach: The proposed CNN cascade-based 3D/2D registration platform comprises three major steps to fit a generic 3D femur model into 2D bi-planar EOS® radiographs: 1) Pose estimation (CNNPose)- a combination of Principal Component Analysis (PCA) and CNN-based 3D/2D similarity registration; 2) 3D shape deformation CNNShape )- a CNN-based 3D displacement estimation of handles followed by Moving Least Square (MLS) shape deformation to extend an as-rigid-as-possible deformation to the entire bone, 3) 3D scale deformation (CNNScale)- a CNN-based 3D scale ratio estimation of handles followed by MLS-based model rescaling. Results: The accuracy of the method is evaluated in comparison to, first, a clinically proved semi-automatic method on 15 patients, and second, Computerized Tomography CT scans of five new patients. In the first vali- dation, the mean ± standard deviation (STD) of the Root Mean Square of point-to-surface distance (RMS-P2S) error is 0.88± 0.29 mm. For the second validation, the mean± STD of RMS-P2S error is 2.70± 0.39 mm. Four clinical measurements of the reconstructed 3D femurs are computed and compared with the first validation set. For each clinical measurement, the Mean Absolute Errors (MAE) is below 1 mm or 1◦. Conclusions: The presented automatic CNN cascade-based framework efficiently registers the generic 3D femur models into bi-planar radiographs. The CNN-based 3D handles displacement and scale estimation eliminates manual-annotations and user-interventions for MLS deformation while maintaining accuracy and speed. This system is applicable for other bones such as the tibia.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
de Guise, Jacques A.
Vázquez, Carlos
Affiliation: Génie des systèmes, Génie logiciel et des technologies de l'information
Date Deposited: 30 Jul 2025 13:27
Last Modified: 12 Aug 2025 20:28
URI: https://espace2.etsmtl.ca/id/eprint/31211

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