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3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

Chen, Cheng, Belavy, David and Zheng, Guoyan 2014, 3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification, Lecture notes in computer science, vol. 8679, pp. 50-58, doi: 10.1007/978-3-319-10581-9_7.

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Title 3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification
Author(s) Chen, Cheng
Belavy, DavidORCID iD for Belavy, David orcid.org/0000-0002-9307-832X
Zheng, Guoyan
Journal name Lecture notes in computer science
Volume number 8679
Start page 50
End page 58
Total pages 9
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014
ISSN 0302-9743
1611-3349
Summary In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.
Language eng
DOI 10.1007/978-3-319-10581-9_7
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
08 INFORMATION AND COMPUTING SCIENCES
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071723

Document type: Journal Article
Collection: School of Exercise and Nutrition Sciences
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