Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning

Javier Villafruela; Sebastian Crites; Bastian Cheng; Christian Knaack; Götz Thomalla; Bijoy K. Menon; Nils D. Forkert

doi:10.1117/12.2255012

3 March 2017 Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning

Javier Villafruela, Sebastian Crites, Bastian Cheng, Christian Knaack, Götz Thomalla, Bijoy K. Menon, Nils D. Forkert

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Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342K (2017) https://doi.org/10.1117/12.2255012
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Stroke is a leading cause of death and disability in the western hemisphere. Acute ischemic strokes can be broadly classified based on the underlying cause into atherosclerotic strokes, cardioembolic strokes, small vessels disease, and stroke with other causes. The ability to determine the exact origin of an acute ischemic stroke is highly relevant for optimal treatment decision and preventing recurrent events. However, the differentiation of atherosclerotic and cardioembolic phenotypes can be especially challenging due to similar appearance and symptoms. The aim of this study was to develop and evaluate the feasibility of an image-based machine learning approach for discriminating between arteriosclerotic and cardioembolic acute ischemic strokes using 56 apparent diffusion coefficient (ADC) datasets from acute stroke patients. For this purpose, acute infarct lesions were semi-atomically segmented and 30,981 geometric and texture image features were extracted for each stroke volume. To improve the performance and accuracy, categorical Pearson’s χ² test was used to select the most informative features while removing redundant attributes. As a result, only 289 features were finally included for training of a deep multilayer feed-forward neural network without bootstrapping. The proposed method was evaluated using a leave-one-out cross validation scheme. The proposed classification method achieved an average area under receiver operator characteristic curve value of 0.93 and a classification accuracy of 94.64%. These first results suggest that the proposed image-based classification framework can support neurologists in clinical routine differentiating between atherosclerotic and cardioembolic phenotypes.

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Javier Villafruela, Sebastian Crites, Bastian Cheng, Christian Knaack, Götz Thomalla, Bijoy K. Menon, and Nils D. Forkert "Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342K (3 March 2017); https://doi.org/10.1117/12.2255012

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KEYWORDS

Ischemic stroke

Image filtering

Diffusion

Image classification

Feature extraction

Neural networks

Machine learning

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