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 language | English (US) |
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Title of host publication | 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 1545-1549 |
Number of pages | 5 |
ISBN (Electronic) | 9781538662496 |
DOIs | |
State | Published - Sep 2019 |
Event | 26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China Duration: Sep 22 2019 → Sep 25 2019 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
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Volume | 2019-September |
ISSN (Print) | 1522-4880 |
Conference
Conference | 26th IEEE International Conference on Image Processing, ICIP 2019 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 9/22/19 → 9/25/19 |
Funding
This study was supported by NIDCD P50 DC012283 and the Center for TranslationalImaging, Northwestern University.
Keywords
- brain symmetry
- convolutional neural networks
- stroke lesion segmentation
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing