Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models

Neubert, Aleš, Fripp, Jurgen, Chandra, Shekhar S., Engstrom, Craig and Crozier, Stuart (2016). Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models. In: Tomaz Vrtovec, Jianhua Yao, Ben Glocker, Tobias Klinder, Alejandro Frangi, Guoyan Zheng and Shuo Li, Computational methods and clinical applications for spine imaging, Third International Workshop and Challenge, CSI 2015. Third International Workshop and Challenge, CSI 2015, Munich, Germany, (149-158). 5 October 2015. doi:10.1007/978-3-319-41827-8_15


Author Neubert, Aleš
Fripp, Jurgen
Chandra, Shekhar S.
Engstrom, Craig
Crozier, Stuart
Title of paper Automated intervertebral disc segmentation using probabilistic shape estimation and active shape models
Conference name Third International Workshop and Challenge, CSI 2015
Conference location Munich, Germany
Conference dates 5 October 2015
Proceedings title Computational methods and clinical applications for spine imaging, Third International Workshop and Challenge, CSI 2015
Series Lecture Notes in Computer Science
Place of Publication Switzerland
Publisher Springer
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1007/978-3-319-41827-8_15
Open Access Status Not yet assessed
ISBN 9783319418261
9783319418278
Editor Tomaz Vrtovec
Jianhua Yao
Ben Glocker
Tobias Klinder
Alejandro Frangi
Guoyan Zheng
Shuo Li
Start page 149
End page 158
Total pages 10
Chapter number 15
Total chapters 15
Language eng
Formatted Abstract/Summary
Automated segmentation of intervertebral discs (IVDs) from magnetic resonance imaging has the potential to enhance the efficiencies of radiological investigations of large clinical and research imaging datasets. This work presents an automated method for localization and 3D segmentation of IVDs that is applied to magnetic resonance imaging of the thoraco-lumbar spine as part of the segmentation challenge at the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. Our initialization method involves multi-atlas registration and a hierarchical conditional shape regression for localization of all imaged lumbar and thoracic discs, and active shape model based 3D segmentation. Comparisons between manual (ground truth) and automated segmentation of 105 disc volumes (T11/T12 - L5/S1) revealed a mean Dice score of 0.896±0.024 and mean absolute square distance of 0.642±0.169 mm. Our automated segmentation approach provided accurate segmentation of IVDs from turbo spine echo images which are highly competitive with leading state-of-the-art 3D segmentation techniques.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Mon, 22 Aug 2016, 23:44:30 EST by Sandrine Ducrot on behalf of School of Human Movement and Nutrition Sciences