Abstract
Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.
Highlights
Stroke is the second leading cause of death and a major cause of disability worldwide [1]
We have developed a novel parallel multiembedding pretrained for better learning during model training instead parametric deep learning feature embedding for useful feature extraction in multi-parametric images of random weight initialization
The proposed parametric feature embedded siamese network (PMFE-SN) in this work is compared with state-of-the art methods on ISLES 2017
Summary
Stroke is the second leading cause of death and a major cause of disability worldwide [1]. 15 million people worldwide suffer a stroke that results in permanent disability, placing a burden on family and community [2]. Most strokes (80%) occur by an unexpected blockage of arteries carrying blood to the brain, causing ischemia, and are called ischemic strokes. The salvageable tissue is termed as “penumbra” and it is the target for reperfusion therapies. The affected area of the brain, the stroke lesion, go through several disease stages that can be categorized as acute (0–24 h), sub-acute (24 h–2 weeks) and chronic (>2 weeks) according to the time passed since stroke onset [3]. The possible options for treatment are largely limited to reperfusion therapies known as thrombolysis and thrombectomy, which must be Diagnostics 2020, 10, 858; doi:10.3390/diagnostics10110858 www.mdpi.com/journal/diagnostics
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