Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices

Abstract : Exploitation of spine radiographs, in particular for 3D spine shape reconstruction of scoliotic patients, is a prerequisite for personalized modelling. Current methods, even though robust enough to be used in clinical routine, still rely on tedious manual adjustments. In this context, this PhD thesis aims toward automated detection of specific vertebrae landmarks in spine radiographs, enabling automated adjustments. In the first part, we developed an original Random Forest based framework for vertebrae corner localization that was applied on sagittal radiographs of both cervical and lumbar spine regions. A rigorous evaluation of the method confirms robustness and high accuracy of the proposed method. In the second part, we developed an algorithm for the clinically-important task of pedicle localization in the thoracolumbar region on frontal radiographs. The proposed algorithm compares favourably to similar methods from the literature while relying on less manual supervision. The last part of this PhD tackled the scarcely-studied task of joint detection, identification and segmentation of spinous processes of cervical vertebrae in sagittal radiographs, with again high precision performance. All three algorithmic solutions were designed around a generic framework exploiting dedicated visual feature descriptors and multi-class Random Forest classifiers, proposing a novel solution with computational and manual supervision burdens aiming for translation into clinical use. Overall, the presented frameworks suggest a great potential of being integrated in current spine 3D reconstruction frameworks that are used in daily clinical routine.
Complete list of metadatas

https://pastel.archives-ouvertes.fr/tel-01763658
Contributor : Abes Star <>
Submitted on : Thursday, April 12, 2018 - 11:01:12 AM
Last modification on : Monday, November 19, 2018 - 2:28:45 PM

File

EBRAHIMI.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01763658, version 2

Collections

Citation

Shahin Ebrahimi. Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices. Bioengineering. Ecole nationale supérieure d'arts et métiers - ENSAM, 2017. English. ⟨NNT : 2017ENAM0050⟩. ⟨tel-01763658v2⟩

Share

Metrics

Record views

361

Files downloads

91