Segmenting tumors and their subregions is a challenging task as demonstrated by the annual BraTS challenge. Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task. In this paper, we present a cascaded pipeline to segment the tumor and its subregions and then we use these results and other clinical features together with image features coming from a pretrained VGG-16 network to predict the survival of the patient. Preliminary results with the training and validation dataset show a promising start in terms of segmentation, while the prediction values could be improved with further testing on the feature extraction part of the network.
@article{arxiv.1810.04274,
title = {Survival prediction using ensemble tumor segmentation and transfer learning},
author = {Mariano Cabezas and Sergi Valverde and Sandra González-Villà and Albert Clérigues and Mostafa Salem and Kaisar Kushibar and Jose Bernal and Arnau Oliver and Xavier Lladó},
journal= {arXiv preprint arXiv:1810.04274},
year = {2018}
}