Mr. Sylvain Gouttard Profile

Sylvain Gouttard

Research Assistant at Univ of Utah

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Publications (4)

Proceedings Article | 13 March 2013 Paper

UNC-Utah NA-MIC DTI framework: atlas based fiber tract analysis with application to a study of nicotine smoking addiction

Audrey Verde, Jean-Baptiste Berger, Aditya Gupta, Mahshid Farzinfar, Adrien Kaiser, Vicki Chanon, Charlotte Boettiger, Hans Johnson, Joy Matsui, Anuja Sharma, Casey Goodlett, Yundi Shi, Hongtu Zhu, Guido Gerig, Sylvain Gouttard, Clement Vachet, Martin Styner

Proceedings Volume 8669, 86692D (2013) https://doi.org/10.1117/12.2007093

KEYWORDS: Diffusion tensor imaging, Control systems, Statistical analysis, Visualization, Image registration, Analytical research, Diffusion, Brain, Diffusion weighted imaging, Neuroimaging

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Proceedings Article | 14 February 2012 Paper

Measures for validation of DTI tractography

Sylvain Gouttard, Casey Goodlett, Marek Kubicki, Guido Gerig

Proceedings Volume 8314, 83140J (2012) https://doi.org/10.1117/12.911546

KEYWORDS: Diffusion tensor imaging, Diffusion, Clouds, Gold, Neuroimaging, Databases, Image registration, Image segmentation, Medical imaging, Binary data

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Proceedings Article | 11 March 2010 Paper

Quality control of diffusion weighted images

Zhexing Liu, Yi Wang, Guido Gerig, Sylvain Gouttard, Ran Tao, Thomas Fletcher, Martin Styner

Proceedings Volume 7628, 76280J (2010) https://doi.org/10.1117/12.844748

KEYWORDS: Diffusion weighted imaging, Diffusion tensor imaging, Diffusion, Brain, Head, Magnetic resonance imaging, Neuroimaging, Visualization, Data acquisition, Signal to noise ratio

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Proceedings Article | 8 March 2007 Paper

Subcortical structure segmentation using probabilistic atlas priors

Sylvain Gouttard, Martin Styner, Sarang Joshi, Rachel Smith, Heather Cody Hazlett, Guido Gerig

Proceedings Volume 6512, 65122J (2007) https://doi.org/10.1117/12.708626

KEYWORDS: Image segmentation, Image registration, Brain, Magnetic resonance imaging, Amygdala, Tissues, Image processing, Skull, Calibration, Reliability

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The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomic analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen, caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas prior and alongside a comprehensive validation. Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a training set of MR images with corresponding manual segmentations. The atlas building computes an average image along with transformation fields mapping each training case to the average image. These transformation fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the registration transformation is applied to the probabilistic maps of each structures, which are then thresholded at 0.5 probability. Using manual segmentations for comparison, measures of volumetric differences show high correlation with our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2 percents over the whole dataset. Overall, these validation and reliability studies show that our method accurately and reliably segments almost all structures. Only the hippocampus and amygdala segmentations exhibit relative low correlation with the manual segmentation in at least one of the validation studies, whereas they still show appropriate dice overlap coefficients.

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