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 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.

[1]  Sameer Antani,et al.  Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities , 2019, Diagnostics.

[2]  Gözde B. Ünal,et al.  Classification of brain tissues as lesion or healthy by 3D convolutional neural networks , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[3]  Hang Su,et al.  Supermarket commodity identification using convolutional neural networks , 2016, 2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT).

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  A. Jagan,et al.  Analysis of MRI based brain tumor identification using segmentation technique , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[7]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Fabio Tozeto Ramos,et al.  Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[9]  Sheng Liu,et al.  Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multi-Spectral MR Image Using Convolutional Neural Network , 2018, IEEE Access.

[10]  L. Padma Suresh,et al.  Tumor region extraction using edge detection method in brain MRI images , 2017, 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT).

[11]  Jing Zhang,et al.  Extreme Weather Recognition Using a Novel Fine-Tuning Strategy and Optimized GoogLeNet , 2017, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[12]  Vinayak Ray,et al.  Image-based fuzzy c-means clustering and connected component labeling subsecond fast fully automatic complete cardiac cycle left ventricle segmentation in multi frame cardiac MRI images , 2016, 2016 International Conference on Systems in Medicine and Biology (ICSMB).

[13]  Parag Kulkarni,et al.  Adaptive Supervised Learning Model for Training Set Selection under Concept Drift Data Streams , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[14]  Kemal Tuncali,et al.  Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy , 2019, IEEE Transactions on Medical Imaging.

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Huangjian Yi,et al.  Semi-Supervised Cerebrovascular Segmentation by Hierarchical Convolutional Neural Network , 2018, IEEE Access.

[17]  Jie Yang,et al.  Brain MRI segmentation with patch-based CNN approach , 2016, 2016 35th Chinese Control Conference (CCC).

[18]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[19]  Liu Hongxin,et al.  FDAR-Net: Joint Convolutional Neural Networks for Face Detection and Attribute Recognition , 2016 .

[20]  H. Wilke,et al.  The benefits of multi-disciplinary research on intervertebral disc degeneration , 2014, European Spine Journal.

[21]  Vipula Singh,et al.  Extraction and description of tumour region from the brain MRI image using segmentation techniques , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Pengfei Duan,et al.  Enhancing AlexNet for Arabic Handwritten words Recognition Using Incremental Dropout , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

[24]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Neural Networks , 2013 .

[25]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Haibing Ren,et al.  FDAR-Net: Joint Convolutional Neural Networks for Face Detection and Attribute Recognition , 2016, 2016 9th International Symposium on Computational Intelligence and Design (ISCID).

[27]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[28]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.