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Authors: Refaat E. Gabr 1 ; Amol Pednekar 2 ; Xiaojun Sun 1 and Ponnada A. Narayana 1

Affiliations: 1 University of Texas Health Science Center at Houston, United States ; 2 Philips Healthcare, United States

ISBN: 978-989-758-009-3

Keyword(s): Multiple Sclerosis, White Mater Lesions, Segmentation, Online Processing.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: With recent advances in the field, magnetic resonance imaging (MRI) has become a powerful quantitative imaging modality for the study of neurological disorders. The quantitative power of MRI is significantly enhanced with multi-contrast and high-resolution techniques. However, those techniques generate large volumes of data which, combined with the sophisticated state-of-the-art image analysis methods, result in a very high computational load. In order to keep the scanner workflow uninterrupted, processing has to be performed off-line leading to delayed access to the quantitative results. This time delay also precludes the evaluation of data quality, and prevents the care giver from using the results of quantitative analysis to guide subsequent studies. We developed a scanner-integrated system for fast online processing of dual-echo fast spin-echo and fluid-attenuated inversion recovery images to quickly classify different brain tissues and generate white matter lesion maps in patient s with multiple sclerosis (MS). The segmented tissues were imported back into the patient database on the scanner for clinical interpretation by the radiologist. The analysis pipeline included rigid-body registration, skull stripping, nonuniformity correction, and tissue segmentation. In six MS patients, the average time taken by the processing pipeline to the final segmentation of the brain into white matter, grey matter, cerebrospinal fluid, and white matter lesions was ~2 min, making it feasible to generate lesion maps immediately after the scan. (More)

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Paper citation in several formats:
E. Gabr, R.; Pednekar, A.; Sun, X. and A. Narayana, P. (2014). Online Brain Tissue Classification in Multiple Sclerosis using a Scanner-integrated Image Analysis Pipeline.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 106-110. DOI: 10.5220/0004660301060110

@conference{visapp14,
author={Refaat E. Gabr. and Amol Pednekar. and Xiaojun Sun. and Ponnada A. Narayana.},
title={Online Brain Tissue Classification in Multiple Sclerosis using a Scanner-integrated Image Analysis Pipeline},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={106-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004660301060110},
isbn={978-989-758-009-3},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Online Brain Tissue Classification in Multiple Sclerosis using a Scanner-integrated Image Analysis Pipeline
SN - 978-989-758-009-3
AU - E. Gabr, R.
AU - Pednekar, A.
AU - Sun, X.
AU - A. Narayana, P.
PY - 2014
SP - 106
EP - 110
DO - 10.5220/0004660301060110

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