1. Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images
- Author
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Karthik V. Sarma, Suzie El-Saden, King Chung Ho, William Speier, Fabien Scalzo, and Corey W. Arnold
- Subjects
convolutional neural network ,perfusion imaging ,Bioengineering ,Perfusion scanning ,Convolutional neural network ,medicine ,Radiology, Nuclear Medicine and imaging ,tissue fate prediction ,screening and diagnosis ,medicine.diagnostic_test ,Contextual image classification ,Artificial neural network ,business.industry ,Deep learning ,Neurosciences ,deep learning ,Biomedical Applications in Molecular, Structural, and Functional Imaging ,Magnetic resonance imaging ,Pattern recognition ,Brain Disorders ,4.1 Discovery and preclinical testing of markers and technologies ,Stroke ,Detection ,Biomedical Imaging ,Deconvolution ,Artificial intelligence ,business ,Feature learning - Abstract
Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of [Formula: see text] , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.
- Published
- 2019