Brain tumor segmentation using large receptive field deep convolutional neural networks

Abstract

Glioblastoma segmentation is an important challenge in medical image processing. State of the art methods make use of convolutional neural networks, but generally employ only few layers and small receptive fields, which limits the amount and quality of contextual information available for segmentation. In this publication we use the well known U-Net architecture to alleviate these shortcomings. We furthermore show that a sophisticated training scheme that uses dynamic sampling of training data, data augmentation and a class sensitive loss allows training such a complex architecture on relatively few data. A qualitative comparison with the state of the art shows favorable performance of our approach.