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Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke
AbstractNowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on â€...
Source: Medical and Biological Engineering and Computing - August 2, 2022 Category: Biomedical Engineering Source Type: research

Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible
Here, we present two different brain diagnostic devices based on microwave technology and the associated two first proof-of-principle measurements that show that the systems can differentiate hemorrhagic from ischemic stroke in acute stroke patients, as well as differentiate hemorrhagic patients from healthy volunteers. The system was based on microwave scattering measurements with an antenna system worn on the head. Measurement data were analyzed with a machine-learning algorithm that is based on training using data from patients with a known condition. Computer tomography images were used as reference. The detection meth...
Source: IEEE Transactions on Biomedical Engineering - October 17, 2014 Category: Biomedical Engineering Source Type: research

Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI
Conclusion: The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics. Significance: Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.
Source: IEEE Transactions on Biomedical Engineering - August 21, 2018 Category: Biomedical Engineering Source Type: research

Identifying risk factors of intracerebral hemorrhage stability using explainable attention model
AbstractSegmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems ’ modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstr apping the global spatial represe...
Source: Medical and Biological Engineering and Computing - December 2, 2021 Category: Biomedical Engineering Source Type: research

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection
Conclusion: The proposed ResAttU-Net-based DL-MITAT method is promising for mitigating the acoustic inhomogeneity issue and performing transcranial brain hemorrhage detection. Significance: This work provides a novel ResAttU-Net-based DL-MITAT paradigm and paves a compelling route for transcranial brain hemorrhage detection as well as other transcranial brain imaging applications.
Source: IEEE Transactions on Biomedical Engineering - July 21, 2023 Category: Biomedical Engineering Source Type: research