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 language | English |
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Title of host publication | Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020 |
Editors | Alba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda |
Pages | 114-119 |
Number of pages | 6 |
ISBN (Electronic) | 9781728194295 |
DOIs | |
State | Published - Jul 2020 |
Event | 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States Duration: Jul 28 2020 → Jul 30 2020 |
Publication series
Name | Proceedings - IEEE Symposium on Computer-Based Medical Systems |
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Volume | 2020-July |
ISSN (Print) | 1063-7125 |
Conference
Conference | 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 7/28/20 → 7/30/20 |
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