A deep symmetry convnet for stroke lesion segmentation

Yanran Wang; Aggelos K. Katsaggelos; Xue Wang; Todd B. Parrish

doi:10.1109/ICIP.2016.7532329

A deep symmetry convnet for stroke lesion segmentation

Yanran Wang, Aggelos K. Katsaggelos, Xue Wang, Todd B. Parrish

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

44 Scopus citations

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.

Original language	English (US)
Title of host publication	2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	111-115
Number of pages	5
ISBN (Electronic)	9781467399616
DOIs	https://doi.org/10.1109/ICIP.2016.7532329
State	Published - Aug 3 2016
Event	23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2016-August
ISSN (Print)	1522-4880

Other

Other	23rd IEEE International Conference on Image Processing, ICIP 2016
Country/Territory	United States
City	Phoenix
Period	9/25/16 → 9/28/16

Funding

This study was supported by NIDCD P50 DC012283 (Project Leader, Cynthia Thompson) and the Center for Translational Imaging, Northwestern University.

Keywords

Brain Quasi-symmetry
Deep Learning
Image Segmentation
MRI
Stroke

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2016.7532329

Cite this

Wang, Y., Katsaggelos, A. K., Wang, X., & Parrish, T. B. (2016). A deep symmetry convnet for stroke lesion segmentation. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 111-115). Article 7532329 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532329

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title = "A deep symmetry convnet for stroke lesion segmentation",

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.",

keywords = "Brain Quasi-symmetry, Deep Learning, Image Segmentation, MRI, Stroke",

author = "Yanran Wang and Katsaggelos, {Aggelos K.} and Xue Wang and Parrish, {Todd B.}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",

year = "2016",

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doi = "10.1109/ICIP.2016.7532329",

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Wang, Y, Katsaggelos, AK, Wang, X & Parrish, TB 2016, A deep symmetry convnet for stroke lesion segmentation. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings., 7532329, Proceedings - International Conference on Image Processing, ICIP, vol. 2016-August, IEEE Computer Society, pp. 111-115, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 9/25/16. https://doi.org/10.1109/ICIP.2016.7532329

A deep symmetry convnet for stroke lesion segmentation. / Wang, Yanran; Katsaggelos, Aggelos K.; Wang, Xue et al.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. IEEE Computer Society, 2016. p. 111-115 7532329 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Wang, Yanran

AU - Katsaggelos, Aggelos K.

AU - Wang, Xue

AU - Parrish, Todd B.

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N2 - 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.

AB - 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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A deep symmetry convnet for stroke lesion segmentation

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