Title: Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks,
Abstract: 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 brain tumor
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-grade tumor cases. Cross-validation has shown that our
method can obtain promising segmentation efficiently.