Survival prediction using ensemble tumor segmentation and transfer learning

Mariano Cabezas; Sergi Valverde; Sandra González-Villà; Albert Clérigues; Mostafa Salem; Kaisar Kushibar; Jose Bernal; Arnau Oliver; Xavier Lladó

arXiv · 1810.04274

Survival prediction using ensemble tumor segmentation and transfer learning

Mariano Cabezas·Sergi Valverde·Sandra González-Villà·Albert Clérigues·Mostafa Salem·Kaisar Kushibar·Jose Bernal·Arnau Oliver·Xavier Lladó

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Abstract

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.

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Mariano Cabezas, Sergi Valverde, Sandra González-Villà, Albert Clérigues, Mostafa Salem, Kaisar Kushibar, Jose Bernal, Arnau Oliver, Xavier Lladó. 2018-10-04. Survival prediction using ensemble tumor segmentation and transfer learning. https://arxiv.org/abs/1810.04274

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