Fully-automated analysis of scoliosis from spinal X-ray images

Abdullah Al Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Kenneth Cheung, Michael To, Zhen Qian, Demetri Terzopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
EditorsAlba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
Pages114-119
Number of pages6
ISBN (Electronic)9781728194295
DOIs
StatePublished - Jul 2020
Event33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States
Duration: Jul 28 2020Jul 30 2020

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2020-July
ISSN (Print)1063-7125

Conference

Conference33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/28/207/30/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Cobb angle
  • Deep learning
  • Progressive U-Net
  • Scoliosis
  • Spine X-ray
  • Vertebrae segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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