NDLI: Towards automatic MRI volumetry for treatment selection in acute ischemic stroke patients

Content Provider	IEEE Xplore Digital Library
Author	Bauer, S. Gratz, P.P. Gralla, J. Reyes, M. Wiest, R.
Copyright Year	2014
Description	Author affiliation: Dept. of Diagnostic & Interventional Neuroradiology, Bern Univ. Hosp., Bern, Switzerland (Gratz, P.P.; Gralla, J.; Wiest, R.) \|\| Inst. for Surg. Technol. & Biomech., Univ. of Bern, Bern, Switzerland (Bauer, S.; Reyes, M.)
Abstract	In many tertiary clinical care centers, decision-making and treatment selection for acute ischemic stroke is based on magnetic resonance imaging (MRI). The “mismatch” concept aims to segregate the infarct core from potentially salvageable hypo-perfused tissue, the so-called penumbra that is determined from a combination of different MRI modalities. Recent studies have challenged the current concept of tissue at risk stratification targeted to identify the best treatment options for every individual patient. Here, we propose a novel, more elaborate image analysis approach that is based on supervised classification methods to automatically segment and predict the extent of the tissue compartments of interest (healthy, infarct, penumbra regions). The output of the algorithm is a label image including quantitative volumetric information about each tissue compartment. The approach has been evaluated on an image dataset of 10 stroke patients and it compared favorably to currently available tools.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	1521
Ending Page	1524
File Size	926228
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Image analysis Biomedical imaging Mathematical model Training
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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