Efficient U-Net CNN with Data Augmentation for MRI Ischemic Stroke Brain Segmentation

Ischemic stroke brains are common cerebrovascular diseases and one of the major issues of long-term disabilities and mortalities in the world. The Detection of ischemic stroke lesions has a significant role in the diagnosis process. In terms of timing and accuracy, automated biomedical segmentation...

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Bibliographic Details
Published inInternational Conference on Control, Decision and Information Technologies (Online) Vol. 1; pp. 724 - 728
Main Authors Aboudi, Fathia, Drissi, Cyrine, Kraiem, Tarek
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.05.2022
Subjects
automated segmentation
CNN
Convolutional neural networks
data augmentation
Image segmentation
Ischemic stroke disease
Magnetic resonance imaging
MRI
Prediction algorithms
Process control
Stroke (medical condition)
Timing
U-Net
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Summary:Ischemic stroke brains are common cerebrovascular diseases and one of the major issues of long-term disabilities and mortalities in the world. The Detection of ischemic stroke lesions has a significant role in the diagnosis process. In terms of timing and accuracy, automated biomedical segmentation from a magnetic resonance imaging modalities has shown to be quite beneficial. In this work, we suggest a deep convolutional neural network (CNN) approach based on U-Net architecture that is able to separate ischemic stroke lesions from normal tissue. Fine-tuning technique has been applied to adopt our aims with U-Net architecture. We used a public dataset ISLES 2015 to evaluate the proposed approach. Experimentally, our network achieved an average Dice Coefficient (DC), and accuracy is 55.77%, 99.96% respectively. Quantitative measures show that U-Net CNN provides significant evaluation metrics. Our proposed network could be used to create an automated tool to segment ischemic stroke brain lesions.
ISSN:2576-3555
DOI:10.1109/CoDIT55151.2022.9804030