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Nicholas J. Tustison

Researcher at University of Virginia

Publications -  163
Citations -  19274

Nicholas J. Tustison is an academic researcher from University of Virginia. The author has contributed to research in topics: Medicine & Image registration. The author has an hindex of 33, co-authored 151 publications receiving 13958 citations. Previous affiliations of Nicholas J. Tustison include University of California, Irvine & University of California, Berkeley.

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Journal ArticleDOI

N4ITK: Improved N3 Bias Correction

TL;DR: A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction with the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field Correction over the original N3 algorithm.
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
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A reproducible evaluation of ANTs similarity metric performance in brain image registration.

TL;DR: This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.