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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
Open AccessArticle

Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 334-340; https://doi.org/10.18383/j.tom.2016.00166
Received: 12 September 2016 / Revised: 8 October 2016 / Accepted: 5 November 2016 / Published: 1 December 2016
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions.
Keywords: FLAIR; convolution; autoencoders; segmentation FLAIR; convolution; autoencoders; segmentation
MDPI and ACS Style

Korfiatis, P.; Kline, T.L.; Erickson, B.J. Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. Tomography 2016, 2, 334-340. https://doi.org/10.18383/j.tom.2016.00166

AMA Style

Korfiatis P, Kline TL, Erickson BJ. Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. Tomography. 2016; 2(4):334-340. https://doi.org/10.18383/j.tom.2016.00166

Chicago/Turabian Style

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J. 2016. "Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning" Tomography 2, no. 4: 334-340. https://doi.org/10.18383/j.tom.2016.00166

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