Sagittal Cervical Spine Landmark Point Detection in X-Ray Using Deep Convolutional Neural Networks
Sagittal Cervical Spine Landmark Point Detection in X-Ray Using Deep Convolutional Neural Networks
Authors
- Ali Pourramezan Fard
- Joe Ferrantelli
- Anne-Lise Dupuis
- Mohammad H. Mahoor
Publication
IEEE Access, 2022; vol. 10: 59413-59427
Article Link
Sagittal Cervical Spine Landmark Point Detection in X-Ray Using Deep Convolutional Neural Networks
Abstract
Sagittal cervical spine alignment measured on X-Ray is a key objective measure for clinicians
caring for patients with a multitude of presenting symptoms. Despite its applications, there has been no research available in this field yet. This paper presents a framework for automatic detection of the Sagittal cervical spine landmark point. Inspired by UNet, we propose an encoder-decoder Convolutional Neural Network (CNN) called PoseNet. In developing our model, we first review the weaknesses of widely used regression loss functions such as the L1, and L2 losses. To address these issues, we propose a novel loss
function specifically designed to improve the accuracy of the localization task under challenging situations (extreme neck pose, low or high brightness and illumination, X-Ray noises, etc.) We validate our model and loss function on a dataset of X-Ray images. The results show that our framework is capable of performing precise sagittal cervical spine landmark point detection even for challenging X-Ray images.
- Ali Pourramezan Fard
- Joe Ferrantelli
- Anne-Lise Dupuis
- Mohammad H. Mahoor
IEEE Access, 2022; vol. 10: 59413-59427
Sagittal cervical spine alignment measured on X-Ray is a key objective measure for clinicians
caring for patients with a multitude of presenting symptoms. Despite its applications, there has been no research available in this field yet. This paper presents a framework for automatic detection of the Sagittal cervical spine landmark point. Inspired by UNet, we propose an encoder-decoder Convolutional Neural Network (CNN) called PoseNet. In developing our model, we first review the weaknesses of widely used regression loss functions such as the L1, and L2 losses. To address these issues, we propose a novel loss
function specifically designed to improve the accuracy of the localization task under challenging situations (extreme neck pose, low or high brightness and illumination, X-Ray noises, etc.) We validate our model and loss function on a dataset of X-Ray images. The results show that our framework is capable of performing precise sagittal cervical spine landmark point detection even for challenging X-Ray images.
