دورية أكاديمية
Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases
| العنوان: | Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases |
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| المؤلفون: | Hai Wang, Shaohua Xu, Kai-bin Fang, Zhang-Sheng Dai, Guo-Zhen Wei, Lu-Feng Chen |
| المصدر: | Journal of Bone Oncology, Vol 42, Iss , Pp 100498- (2023) |
| بيانات النشر: | Elsevier, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Diseases of the musculoskeletal system LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Radiomics, Deep learning, Spinal tumor, CE-MRI, improved U-Net, Inception-ResNet, Diseases of the musculoskeletal system, RC925-935, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | Objective: The objective of this study was to investigate the use of contrast-enhanced magnetic resonance imaging (CE-MRI) combined with radiomics and deep learning technology for the identification of spinal metastases and primary malignant spinal bone tumor. Methods: The region growing algorithm was utilized to segment the lesions, and two parameters were defined based on the region of interest (ROI). Deep learning algorithms were employed: improved U-Net, which utilized CE-MRI parameter maps as input, and used 10 layers of CE images as input. Inception-ResNet model was used to extract relevant features for disease identification and construct a diagnosis classifier. Results: The diagnostic accuracy of radiomics was 0.74, while the average diagnostic accuracy of improved U-Net was 0.98, respectively. the PA of our model is as high as 98.001%. The findings indicate that CE-MRI based radiomics and deep learning have the potential to assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor. Conclusion: CE-MRI combined with radiomics and deep learning technology can potentially assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor, providing a promising approach for clinical diagnosis. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2212-1374 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2212137423000313; https://doaj.org/toc/2212-1374 |
| DOI: | 10.1016/j.jbo.2023.100498 |
| URL الوصول: | https://doaj.org/article/c815f8f758f54936a81cc887662ab401 |
| رقم الأكسشن: | edsdoj.815f8f758f54936a81cc887662ab401 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 22121374 |
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| DOI: | 10.1016/j.jbo.2023.100498 |