| 摘要(英) |
Cerebral Microbleed (CMB) is a small chronic cerebral hemorrhage. Recent studies have gradually confirmed that it has a significant relationship with factors such as stroke and intellectual disability. Early detection and treatment have become an increasingly important issue. Cerebral microbleeds are most evident on Susceptibility-Weighted Images (SWI) and usually appear as black homogeneous circles. The purpose of this study is to develop a method for automatically detecting cerebral microbleeds by analyzing SWI images and assisting clinicians to identify them with an artificial intelligence model.
The method proposed in this study is mainly divided into three stages. The first stage is to use Mask R-CNN, which runs faster and can perform segmentation directly, to extract the parenchymal brain from the SWI image. The second stage uses YOLO to screen for candidate CMBs within the scope of the parenchymal brain area. The third stage uses a 3D convolutional neural network to classify the candidate CMBs to find the real CMBs. Finally, the functions of the three stages are integrated and presented through a graphical user interface to provide convenience and efficiency for the users.
The results of the study showed that the accuracy of SWI image parenchymal brain extraction was 98%, the sensitivity was 93%, and the Dice coefficient was 95%; the sensitivity of detecting candidate CMBs on the YOLO network was 90%, and the average number of false positives per patient was 78.19 CMBs. The final 3D CNN model showed that CMB and non-CMB in most patients could be identified and classified, with a sensitivity of 85% and an average false positive average of 3.73 CMBs per patient.
In summary, this research used three types of neural networks in series for automatic detection of CMBs. This method can largely reduce the burden of the clinicians in detecting CMBs. The output quality rendered by this method is good and clinically useful. |
| 參考文獻 |
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