چکیده 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
کلیدواژهها
نویسندگان
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
شیوه ارجاع
sanati, Shiva and Nosrati, Neda,1399,FLAIR Brain Tumor Segmentation Using U-Net Convolutional Neural Network,Eleventh National Conference on Computer Science and Engineering and Information Technology,Babol
ارائهشده در
مجموعه مقالات یازدهمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات27 آذر 1399 · بابل