Fully automatic cervical vertebrae segmentation framework for X-ray images

Computer Methods and Programs in Biomedicine
S M Masudur Rahman Al ArifGreg Slabaugh

Abstract

The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved.

Citations

Oct 20, 2018·Journal of Digital Imaging·Yujing ZhouXiubao Sui
Apr 25, 2020·Asian Spine Journal·Parisa AzimiAli Montazeri
Nov 7, 2019·Journal of Clinical Medicine·Chi-Hung WengTsung-Ting Tsai
Oct 12, 2019·Journal of Digital Imaging·Yuan LiuYong Hu
Aug 29, 2020·European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·Peter BernsteinMarkus Wacker
Feb 6, 2021·Computer Methods and Programs in Biomedicine·Lei Zhang, Huan Wang
Dec 1, 2020·Computer Methods and Programs in Biomedicine·Kang Cheol KimJin Keun Seo
Oct 31, 2021·European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·Yu SunYong Hai

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