Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning
| Autor: | Lin Wang, Yongsheng Chen, Sean K. Sethi, E. Mark Haacke, Saifeng Liu, David Utriainen, Chao Chai, Shuang Xia |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Adolescent Traumatic brain injury Computer science Cognitive Neuroscience Convolutional neural network Sensitivity and Specificity 050105 experimental psychology 03 medical and health sciences Young Adult 0302 clinical medicine Deep Learning Image Interpretation Computer-Assisted medicine Medical imaging Dementia Humans 0501 psychology and cognitive sciences Child Stroke Aged Cerebral Hemorrhage Retrospective Studies Aged 80 and over Artificial neural network business.industry Deep learning 05 social sciences Brain Quantitative susceptibility mapping Pattern recognition Middle Aged medicine.disease Magnetic Resonance Imaging Neurology Child Preschool Susceptibility weighted imaging Artificial intelligence business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | NeuroImage. 198 |
| ISSN: | 1095-9572 |
| Popis: | Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic algorithms for CMB detection are usually limited by large number of false positives. In this study, we present a two-stage CMB detection framework which contains a candidate detection stage based on a 3D fast radial symmetry transform of the composite images from Susceptibility Weighted Imaging (SWI), and a false positive reduction stage based on deep residual neural networks using both the SWI and the high-pass filtered phase images. While the SWI images provide exquisite sensitivity to the presence of blood products, the high-pass filtered phase images enable the differentiation of diamagnetic calcifications from paramagnetic microbleeds. The deep learning model was trained using 154 data sets, and the best models were selected using 25 validation data sets. Finally, the models were tested using 41 cases, including 13 hemodialysis cases, 9 traumatic brain injury cases, 9 stroke cases and 10 healthy controls. Using 3D SWI and high-pass filtered phase images as input, the best model led to a sensitivity of 95.8%, a precision of 70.9%, and 1.6 false positives per case. This model achieved similar performance to the most experienced human rater and outperformed recently reported CMB detection methods. This study demonstrates the potential of applying deep learning techniques to medical imaging for improving efficiency and accuracy in diagnosis. |
| Databáze: | OpenAIRE |
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