Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Understand

Quantitative analysis of brain tumors is critical for clinical decision making.

• While manual segmentation is tedious, time consuming and subjective, this task is at the same time very challenging to solve for automatic segmentation methods.
• In this paper we present our most recent effort on developing a robust segmentation algorithm in the form of a convolutional neural network.
• Our network architecture was inspired by the popular U-Net and has been carefully modified to maximize brain tumor segmentation performance.