3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

Chu, Chengwen; Belavy, D.; Zheng, Guoyan (2014). 3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification. In: Wu, Guorong; Zhang, Daoqiang; Zhou, Luping (eds.) MLMI 2014, LNCS 8679. Lecture Notes in Computer Science: Vol. 8679 (pp. 50-58). Cham: Springer 10.1007/978-3-319-10581-9_7

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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.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Chu, Chengwen and Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

0302-9743

ISBN:

978-3-319-10580-2

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

German

Submitter:

Guoyan Zheng

Date Deposited:

01 May 2015 15:39

Last Modified:

25 Nov 2015 10:48

Publisher DOI:

10.1007/978-3-319-10581-9_7

BORIS DOI:

10.7892/boris.67983

URI:

https://boris.unibe.ch/id/eprint/67983

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