Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas
Automated segmentation of brain tumors in multi-channel Magnetic Resonance Image (MRI) is a challenging task. Heterogeneous appearance of brain tumors in MRI poses critical challenges in diagnosis, prognosis and survival prediction. In this paper, we present a novel approach for glioma tumor segmentation and survival prediction with Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model using 3D patch based U-Net model in Brain Tumor Segmentation (BraTS) challenge 2018. Radiomics feature extraction and classification was done on segmented tumor for overall survival (OS) prediction task. Preliminary results of DRAG model on BraTS 2018 validation dataset demonstrated that the proposed method achieved a good performance with Dice scores as 0.88, 0.83 and 0.75 for whole tumor, tumor core and enhancing tumor, respectively. For survival prediction, 57.1% accuracy was achieved on the validation dataset. The proposed DRAG model was one of the top performing models and accomplished third place for OS prediction task in BraTS 2018 challenge.
KeywordsBrain Tumor Segmentation Gliomas Convolutional Neural Networks Radiomics MRI Radiogenomics Survival prediction
This work was supported by Ministry of Electronics and Information Technology, Govt. of India under Visvesvaraya PhD scheme with implementation reference number: PhD-MLA/4(67/2015-16). Authors are thankful to Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded and Tata Memorial Centre, Mumbai.
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