Tumor segmentation of magnetic resonance images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully-automated deep learning method for tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole-tumor (WT), tumor-core (TC) and enhancing-tumor (ET). Each group was trained using different approaches and loss-functions. The output segmentations of a particular label from their respective networks from the three groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on both the BraTS2019 validation and testing datasets. The segmentation networks achieved average dice-scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively on the validation dataset and achieved dice-scores of 0.877, 0.835 and 0.803 for WT, TC and ET respectively on the testing dataset. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244 on the validation dataset and achieved an accuracy score of 0.51 and MSE of 455500 on the testing dataset. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.