Segmentation Squeeze-and-Excitation Blocks in Stroke Lesion Outcome Prediction

Amorim, Joana; Pinto, Adriano; Pereira, Sérgio

doi:10.1109/enbeng.2019.8692549

2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG) 2019

DOI: 10.1109/enbeng.2019.8692549

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Segmentation Squeeze-and-Excitation Blocks in Stroke Lesion Outcome Prediction

Joana Amorim

Adriano Pinto

Sérgio Pereira

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Cited by 2 publications

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“…The majority of the proposed methods for stroke lesion prediction only considers the standard parametric maps [19]. Only recently, perfusion DSC-MRI has been considered for final infarct stroke prediction [20]- [24]. Amorim et al…”

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confidence: 99%

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Pinto

Amorim

Hakim³

et al. 2021

IEEE Access

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Stroke is the second most common cause of death in developed countries. Rapid clinical assessment and intervention have a major impact on preventing infarct growth and consequently on patients' quality of life. Clinical interventions aim to restore perfusion deficits via pharmaceutical or mechanical intervention. Regardless of which reperfusion procedure is used, clinicians need to consider the risks and benefits based on multi-modal neuroimaging studies, such as MRI scans, as well as their own clinical experience. This intricate decision-making process would benefit from an automatic prediction of the final infarct, which would provide a estimation of tissue that will probably infarct. This paper introduces a deep learning method to automatically predict ischemic stroke tissue outcome. The authors propose an end-to-end deep learning architecture that combines information from perfusion dynamic susceptibility MRI, alongside perfusion and diffusion parametric maps. We aim to automatically extract features from the raw perfusion DSC-MRI to further complement the information gleaned from standard parametric maps, and to overcome the loss of information that can occur during perfusion postprocessing. Combining both data types in a single architecture, with dedicated paths, we achieve competitive results when predicting the final stroke infarct core lesion in the publicly available ISLES 2017 dataset.

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confidence: 99%

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Pinto

Amorim

Hakim³

et al. 2021

IEEE Access

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Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review

Shurrab,

Guerra-Manzanares,

Magid

et al. 2024

IEEE J. Biomed. Health Inform.

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Segmentation Squeeze-and-Excitation Blocks in Stroke Lesion Outcome Prediction

Cited by 2 publications

References 10 publications

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI With Parametric Maps Using Deep Learning

Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review

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