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FLAIR Brain Tumor Segmentation Using U-Net Convolutional Neural Network

عنوان مقاله: FLAIR Brain Tumor Segmentation Using U-Net Convolutional Neural Network
شناسه ملی مقاله: CECCONF11_020
منتشر شده در یازدهمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات در سال 1399
مشخصات نویسندگان مقاله:

Shiva sanati - PhD Student, Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Neda Nosrati - MSC, Computer Engineering Department, Faculty of Engineering, Islamic Azad university of mashhad, Mashhad, Iran

خلاصه مقاله:
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of braintumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study,we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks.Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-gradetumor cases. Crossvalidation has shown that our method can obtain promising segmentation efficiently

کلمات کلیدی:
brain tumor, magnetic resonance imaging (MRI), U-Net based deep convolutional networks

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1134756/