title: Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.
abstract: 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.
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota.
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Korfiatis
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Kline
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota.
authors.forename: Bradley J
authors.initials: BJ
authors.lastname: Erickson
keywords: FLAIR; autoencoders; convolution; segmentation