Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning
Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerot...
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/11/2573 |
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| author | Yi-Zeng Hsieh Yu-Cin Luo Chen Pan Mu-Chun Su Chi-Jen Chen Kevin Li-Chun Hsieh |
| author_facet | Yi-Zeng Hsieh Yu-Cin Luo Chen Pan Mu-Chun Su Chi-Jen Chen Kevin Li-Chun Hsieh |
| author_sort | Yi-Zeng Hsieh |
| collection | DOAJ |
| description | Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient. |
| first_indexed | 2024-04-11T13:00:02Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:00:02Z |
| publishDate | 2019-06-01 |
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| series | Sensors |
| spelling | doaj.art-fd1da3a6d4b64949b54dbd0ac69f07452022-12-22T04:22:59ZengMDPI AGSensors1424-82202019-06-011911257310.3390/s19112573s19112573Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep LearningYi-Zeng Hsieh0Yu-Cin Luo1Chen Pan2Mu-Chun Su3Chi-Jen Chen4Kevin Li-Chun Hsieh5Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Computer Science & Information Engineering, National Central University, Taoyuan City 32001, TaiwanDepartment of Radiology, Shuang Ho Hospital, New Taipei City 23561, TaiwanDepartment of Medical Imaging, Taipei Medical University Hospital, Taipei City 110, TaiwanMagnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient.https://www.mdpi.com/1424-8220/19/11/2573MRI-sensor-based imagebiomarkers detectioncerebral small vessel diseasedeep learningconvolutional neural networkcomputer-aided diagnosis system |
| spellingShingle | Yi-Zeng Hsieh Yu-Cin Luo Chen Pan Mu-Chun Su Chi-Jen Chen Kevin Li-Chun Hsieh Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning Sensors MRI-sensor-based image biomarkers detection cerebral small vessel disease deep learning convolutional neural network computer-aided diagnosis system |
| title | Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning |
| title_full | Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning |
| title_fullStr | Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning |
| title_full_unstemmed | Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning |
| title_short | Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning |
| title_sort | cerebral small vessel disease biomarkers detection on mri sensor based image and deep learning |
| topic | MRI-sensor-based image biomarkers detection cerebral small vessel disease deep learning convolutional neural network computer-aided diagnosis system |
| url | https://www.mdpi.com/1424-8220/19/11/2573 |
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