摘要(英) |
Cerebrovascular diseases including brain strokes are the fourth leading cause of death in Taiwan. Acute ischemic strokes account for more than 80% of brain strokes. To date, the major treatment method is to inject recombinant tissue plasminogen activator (rt-PA). This treatment must be done within 3 hours after brain stroke, because injection of rt-PA later than 3 hours post-stroke may cause life-threatening cerebral hemorrhages. Hence, an accurate diagnosis of acute ischemic stroke and prompt decision for urgent treatment are very important. In clinical medicine, magnetic resonance imaging (MRI) is the most powerful tool for visualizing stroke lesions to diagnose acute ischemic stroke.
To quantify stroke infarction from MRIs, the time-consuming and cumbersome manual labeling is still the main method clinically available. This paper proposes a method for rapidly and automatically detecting infarction on DWI, which is the most sensitive to brain infarct among different MR images. This method can roughly be divided into two stages. The first stage is to use Mask R-CNN to extract brain parenchyma from DWI to eliminate the skull and extracranial noise. The second stage is to use 3D multi-scale CNN to segment brain infarction on DWI brain parenchyma. Multi-scale network architecture can learn both rough infarction positions and detail features. Before training the two neural network models, image preprocessing such as image normalization, image resampling, and data augmentation will be performed.
This study used 218 DWI scans collected from Taipei Veterans General Hospital. Among these scans, 200 were used for a 5-fold cross-validation, which resulted in a 74.2% average Dice similarity coefficient (DSC), a 77.5% average precision, and a 76.2% average recall. The remaining 18 scans were used as the internal test set to test the five generated models. The internal test resulted in a 74.6% average DSC, a 76.7% average precision, and a 76.8% average recall. Furthermore, these models were tested with 66 DWI scans from Landseed International Hospital as an external test set. The average results of DSC, precision, and recall rate were 68.9%, 65.1%, and 77.8%, respectively. |
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