Data Augmentation to Increase Multi-Site Robustness for Convolutional Neural Networks - A case study on MRI segmentation of target and organs at risk for prostate cancer
Organ segmentation on magnetic resonance (MR) images for dose planning on cancer patients is a time consuming process that can be automatized by using convolutional neural networks (CNNs). MR images vary greatly in characteristics across acquisition sites, which affects CNN segmentation performance. In this master thesis, augmentation methods were tested and implemented to increase the robustness
