Abstract
Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. A fully automatic, accurate and robust segmentation of intervertebral discs in magnetic resonance (MR) images is therefore a prerequisite for the computer-aided diagnosis and quantification of intervertebral disc degeneration. In this paper, we propose an automated framework for intervertebral disc segmentation from MR spine images, in which intervertebral disc detection is performed by a landmark-based approach and segmentation by a deformable model-based approach using the self-similarity context (SSC) descriptor. The performance was evaluated on three publicly available databases of MR spine images that represent the training, on-line and on-site testing data for the intervertebral disc localization and segmentation challenge in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015, yielding an overall mean Euclidean distance of 2.4, 1.7 and 2.2 mm for intervertebral disc localization, and an overall mean Dice coefficient of 92.5, 91.5 and 92.0 % for intervertebral disc segmentation for training, on-line and on-site testing data, respectively.
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This work was supported by the Slovenian Research Agency (ARRS) under grants P2-0232, J2-5473 and J7-6781.
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Korez, R., Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T. (2016). Deformable Model-Based Segmentation of Intervertebral Discs from MR Spine Images by Using the SSC Descriptor. In: Vrtovec, T., et al. Computational Methods and Clinical Applications for Spine Imaging. CSI 2015. Lecture Notes in Computer Science(), vol 9402. Springer, Cham. https://doi.org/10.1007/978-3-319-41827-8_11
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