Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

• Published in: Medical Image Understanding and Analysis Vol. 723; pp. 506 - 517
• Main Authors: Dong, Hao, Yang, Guang, Liu, Fangde, Mo, Yuanhan, Guo, Yike
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Communications in Computer and Information Science
• ISBN: 9783319609638; 3319609637
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-60964-5_44

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.