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Automatic segmentation of the spinal cord using continuous max flow with cross-sectional similarity prior and tubularity features

Pezold, Simon and Fundana, Ketut and Amann, Michael and Andelova, Michaela and Pfister, Armanda and Sprenger, Till and Philippe. C. Cattin, . (2015) Automatic segmentation of the spinal cord using continuous max flow with cross-sectional similarity prior and tubularity features. In: Recent advances in computational methods and clinical applications for spine imaging. Cham, pp. 107-118.

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Official URL: http://edoc.unibas.ch/dok/A6348361

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Abstract

Segmenting tubular structures from medical image data is a common problem; be it vessels, airways, or nervous tissue like the spinal cord. Many application-specific segmentation techniques have been proposed in the literature, but only few of them are fully automatic and even fewer approaches maintain a convex formulation. In this paper, we show how to integrate a cross-sectional similarity prior into the convex continuous max-flow framework that helps to guide segmentations in image regions suffering from noise or artefacts. Furthermore, we propose a scheme to explicitly include tubularity features in the segmentation process for increased robustness and measurement repeatability. We demonstrate the performance of our approach by automatically segmenting the cervical spinal cord in magnetic resonance images, by reconstructing its surface, and acquiring volume measurements.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Center for medical Image Analysis & Navigation (Cattin)
UniBasel Contributors:Cattin, Philippe Claude
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:Springer
Note:Note: Full paper presented at the MICCAI 2014 workshop on Computational Methods and Clinical Applications for Spine Imaging -- Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:03 Jul 2015 08:53
Deposited On:03 Jul 2015 08:53

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