A Deep Learning Method for MRI Brain Tumor Segmentation

摘要

In recent years, computer-aided diagnosis has successfully been applied in various clinical problems. By this way, physicians can detect diseases with a high accuracy. Deep learning has emerged as an effective tool for computer vision. Therefore, this paper proposes a new deep learning-based segmentation method for brain tumors on MRI images. Brain tumor is a fatal disease which may occur in any position of human brains. The change of brain tumors with shapes and sizes makes it difficult for precise segmentation. We design a novel architecture of fully convolutional networks to automatically segment brain tumors and the patch-wise training trick is exploited to train the model, which could capture local information. The experiments are performed on the MCCAI Brain Tumor Segmentation challenge 2015 dataset. The proposed method achieves an average dice score of 0.82 (0.76, 0.73) for the whole tumor (core tumor, enhancing tumor) regions.