Keywords: Deep Learning, Hippocampus Segmentation
TL;DR: A consensus of a ensemble of modified UNet based CNNs performs segmentation of the hippocampus, trained over atrophied hippocampus
Abstract: Hippocampus segmentation plays a key role in diagnosing various brain disorders. Nowadays, segmentation is a manual, time consuming task and considered to be the gold-standard when evaluating automated methods. For years the best performing automatic methods were multi atlas based with 80 to 85% DICE and time consuming, but machine learning methods are recently rising with promising time and accuracy performance. In this work, a novel method for hippocampus segmentation is presented, based on the consensus of tri-planar U-Net inspired CNNs, with some modifications based on successful CNNs of the literature, and a patch extraction technique employing data from neighbor patches. Our in-house dataset has hippocampus atrophies resulted from epilepsy surgery treatment. Our method (labeled e2dhipseg) achieves cutting edge performance of 96% DICE in our test data. Our method was also compared to other recent methods in the public ADNIand HARP datasets.
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