چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| ابوالحسن شاکری باویل علیائی | چهارم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | White matter hyperintensities (WMHs) on FLAIR MRI are critical indicators of cerebrovascular dysfunction associated with elevated risks of stroke, dementia, and death. Current automated segmentation methods suffer from false positive detection in periventricular regions, failing to distinguish normal aging-related hyperintensities from pathologically significant lesions, which reduces clinical applicability and diagnostic accuracy. This study investigates whether training deep learning models to explicitly differentiate between normal and abnormal WMH improves pathological WMH segmentation performance compared to traditional binary approaches. Four state-of-the-art architectures (U-Net, Attention U-Net, DeepLabV3Plus, Trans-U-Net) were evaluated across two training scenarios using 1,974 FLAIR images from 100 MS patients with expert-annotated ground truths. Scenario 1 employed binary training (background vs abnormal WMH), while Scenario 2 utilized three-class training (background, normal WMH, abnormal WMH). Statistical analysis included paired t-tests and Cohen's d effect size calculations. U-Net achieved the most substantial improvement in Scenario 2 with 55.6% increase in Dice coefficient (0.693 vs 0.443) and 131% precision enhancement (p < 0.0001, Cohen's d = 0.971). Traditional CNN-based architectures demonstrated larger effect sizes than transformer-based models. The three-class training approach significantly enhances pathological WMH segmentation while maintaining clinical feasibility, providing a validated framework for improving automated neuroimaging tools' diagnostic utility. Keywords— White matter hyperintensities (WMH); deep learning; medical image segmentation; FLAIR MRI; multi-class classification; U-Net; pathological segmentation; neuroimaging |
| کلمات کلیدی | — White matter hyperintensities (WMH); deep learning; medical image segmentation; FLAIR MRI; multi-class classification; U-Net; pathological segmentation; neuroimaging |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| 2_ICBME.pdf | 1404/10/16 | 790402 | دانلود |
| 6666666 001.jpg | 1404/10/21 | 889175 | دانلود |
