@inproceedings{Yanran2016,
abstract = {Stroke is one of the leading causes of death and disability. Clinically, to establish stroke patient prognosis, an accurate delineation of brain lesion is essential, which is time consuming and prone to subjective errors. In this paper, we propose a novel method call Deep Lesion Symmetry ConvNet to automatically segment chronic stroke lesions using MRI. An 8-layer 3D convolutional neural network is constructed to handle the MRI voxels. An additional CNN stream using the corresponding symmetric MRI voxels is combined, leading to a significant improvement in system performance. The high average dice coefficient achieved on our dataset based on data collected from three research labs demonstrates the effectiveness of our method.},
author = {Wang, Yanran and Katsaggelos, Aggelos K. and Wang, Xue and Parrish, Todd B.},
booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2016.7532329},
isbn = {978-1-4673-9961-6},
issn = {15224880},
keywords = {Brain Quasi-symmetry,Deep Learning,Image Segmentation,MRI,Stroke},
month = {sep},
pages = {111--115},
publisher = {IEEE},
title = {{A deep symmetry convnet for stroke lesion segmentation}},
url = {http://ieeexplore.ieee.org/document/7532329/},
volume = {2016-Augus},
year = {2016}
}

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