@inproceedings{kamnitsas2016deepmedic,
author = {Kamnitsas, Konstantinos and Ferrante, Enzo and Parisot, Sarah and Ledig, Christian and Nori, Aditya and Criminisi, Antonio and Rueckert, Daniel and Glocker, Ben},
title = {DeepMedic for Brain Tumor Segmentation},
booktitle = {MICCAI Brain Lesion Workshop},
year = {2016},
month = {October},
abstract = {Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.},
url = {http://approjects.co.za/?big=en-us/research/publication/deepmedic-brain-tumor-segmentation/},
pages = {138-149},
}