In this paper, we use a Fully Convolutional Neural Network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI). A fully automatic, voxel based classification was achieved by training a 23 layer deep FCNN on 2-D slices extracted from patient volumes. The network was trained on slices extracted from 130 patients and validated on 50 patients. For the task of survival prediction, texture and shape based features were extracted from T1 post contrast volume to train an Extremely Gradient Boosting (XGBoost) regressor. On the BraTS 2017 validation set, the proposed scheme achieved a mean whole tumor, tumor core and active dice score of 0.83, 0.69 and 0.69 respectively, while for the task of overall survival prediction, the proposed scheme achieved an accuracy of 52%.
CITATION STYLE
Alex, V., Safwan, M., & Krishnamurthi, G. (2018). Automatic segmentation and overall survival prediction in gliomas using fully convolutional neural network and texture analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10670 LNCS, pp. 216–225). Springer Verlag. https://doi.org/10.1007/978-3-319-75238-9_19
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