EPRA U-Net: an efficient pyramid residual attention framework for accurate infarct segmentation in diffusion-weighted MRI
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Le résumé fourni par la source
Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events. This study presents EPRA U-Net (Efficient Pyramid Residual Attention U-Net), a task-specific integrated architecture for efficient and accurate infarct segmentation of DWI images. In the proposed architecture, an EfficientNet-based encoder was used as a hierarchical feature extractor with a minimized parameterization. In addition, a Residual-Recurrent (R2) block (recurrent unrolling step t = 2, following the original formulation) and Atrous Spatial Pyramid Pooling (ASPP) were integrated to enhance the performance of spatial dependency modeling. Additionally, a dual attention mechanism was incorporated to highlight lesion-related activations while concurrently enabling the suppression of extraneous background responses. To prioritize lesion detection consistent with clinical imperative, a Tversky loss function (α = 0.4, β = 0.6) was adopted, thereby emphasizing the sensitivity of detection over its specificity during the optimization process. Experimental evaluations were conducted utilizing an in-house dataset comprising 167 patients with 4,895 DWI slices; subsequently, the performance of the proposed EPRA U-Net was assessed in comparison with state-of-the-art models, specifically UNet++, DeepLabV3+, and TransUNet. The experimental results suggest that EPRA U-Net attained superior performance, evidenced by a pixel-aggregated Dice of 0.8984, a per-sample Dice of 0.9469, an IoU of 0.8155, a Recall of 0.8887, a Lesion F1 of 0.9378, and an HD95 of 11.62 px (median: 2.00 px). Furthermore, a clear reduction in the rate of missed lesions, specifically by 16%, 25%, and 29%, was observed when compared with UNet++, DeepLabV3+, and TransUNet, respectively. Additionally, a comprehensive ablation study was systematically conducted. These findings suggest that the proposed architecture affords a more robust and accurate infarct segmentation, consequently facilitating a more reliable estimation of treatment eligibility.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- EPRA U-Net: an efficient pyramid residual attention framework for accurate infarct segmentation in diffusion-weighted MRI
- Date Crossref
- 24/07/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Yozgat Bozok Üniversitesi pays non établi dans la noticeUniversité ou école supérieure
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Izmir University pays non établi dans la noticeUniversité ou école supérieure
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Bakırçay Üniversitesi pays non établi dans la noticeUniversité ou école supérieure
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Fırat University pays non établi dans la noticeUniversité ou école supérieure
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Qatar University Department of Electrical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Yozgat Bozok University Department of Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Izmir Bakircay University Department of Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Queen Mary’s Digital Environment Research Institute (DERI) pays non établi dans la noticeStructure de recherche
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Faculty of Medicine Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
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Technology Faculty Department of Digital Forensics Engineering pays non établi dans la noticeUniversité ou école supérieure
Yozgat Bozok Üniversitesi, Izmir University et Bakırçay Üniversitesi, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.