A 3D Cross-Hemisphere Neighborhood Difference Convnet for Chronic Stroke Lesion Segmentation

Yan Ran Wang, Hengkang Wang, Sophia Chen, Aggelos K. Katsaggelos, Adam Martersteck, James Higgins, Virginia B. Hill, Todd B. Parrish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Chronic stroke lesion segmentation on magnetic resonance imaging scans plays a critical role in helping physicians to determine stroke patient prognosis. We propose a convolutional neural network (CNN) segmentation network - a 3D Cross-hemisphere Neighborhood Difference ConvNet -which utilizes brain symmetry. The main novelty of this network lies on a 3D cross-hemisphere neighborhood difference layer which introduces robustness to position and scale in brain symmetry. Such robustness is important in helping the CNN distinguish between minute hemispheric differences and the asymmetry caused by a lesion. We compared our model with the state-of-the-art method using a chronic stroke lesion segmentation database. Our results demonstrate the effectiveness of the proposed model and the benefit of a CNN that combines the physiologically based information, that is, the brain symmetry property.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages1545-1549
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • brain symmetry
  • convolutional neural networks
  • stroke lesion segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Wang, Y. R., Wang, H., Chen, S., Katsaggelos, A. K., Martersteck, A., Higgins, J., Hill, V. B., & Parrish, T. B. (2019). A 3D Cross-Hemisphere Neighborhood Difference Convnet for Chronic Stroke Lesion Segmentation. In 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (pp. 1545-1549). [8803092] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8803092