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.

Keywords

• Neck landmark point detection
• landmark point detection
• intensity aware loss
• custom loss function
• medical image processing
• X-Ray image processing