Share Email Print

Proceedings Paper

Intervertebral disc segmentation in MR images with 3D convolutional networks
Author(s): Robert Korez; Bulat Ibragimov; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and debilitating low back pain. The main diagnostic challenge is to locate the pain generator, and degenerated IVDs have been identified to act as such. Accurate and robust segmentation of IVDs is therefore a prerequisite for computer-aided diagnosis and quantification of IVD degeneration, and can be also used for computer-assisted planning and simulation in spinal surgery. In this paper, we present a novel fully automated framework for supervised segmentation of IVDs from three-dimensional (3D) magnetic resonance (MR) spine images. By considering global intensity appearance and local shape information, a landmark-based approach is first used for the detection of IVDs in the observed image, which then initializes the segmentation of IVDs by coupling deformable models with convolutional networks (ConvNets). For this purpose, a 3D ConvNet architecture was designed that learns rich high-level appearance representations from a training repository of IVDs, and then generates spatial IVD probability maps that guide deformable models towards IVD boundaries. By applying the proposed framework to 15 3D MR spine images containing 105 IVDs, quantitative comparison of the obtained against reference IVD segmentations yielded an overall mean Dice coefficient of 92.8%, mean symmetric surface distance of 0.4 mm and Hausdorff surface distance of 3.7 mm.

Paper Details

Date Published: 24 February 2017
PDF: 10 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013306 (24 February 2017); doi: 10.1117/12.2254069
Show Author Affiliations
Robert Korez, Univ. of Ljubljana (Slovenia)
Bulat Ibragimov, Stanford Univ. School of Medicine (United States)
Boštjan Likar, Univ. of Ljubljana (Slovenia)
Franjo Pernuš, Univ. of Ljubljana (Slovenia)
Tomaž Vrtovec, Univ. of Ljubljana (Slovenia)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?