A DEEP SYMMETRY CONVNET FOR STROKE LESION SEGMENTATION

TitleA DEEP SYMMETRY CONVNET FOR STROKE LESION SEGMENTATION
Publication TypeConference Paper
Year of Publication2016
AuthorsWang, Y., X. Wang, A. K. Katsaggelos, and T. B. Parrish
Conference NameIEEE International Conference on Image Processing
Date Published02/2016
Conference LocationPhoenix, Arizona, USA
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.

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