A major challenge in brain tumor treatment planning and quantita- tive 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 perfor- mance 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. 1
CITATION STYLE
Raza, S. E. A., Cheung, L., Epstein, D., Pelengaris, S., Khan, M., & B, N. M. R. (2017). MIMONet : Gland Segmentation Using Neural Network. Computer Methods and Programs in Biomedicine, 1(d), 698–706.
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