Automatic segmentation and overall survival prediction in gliomas using fully convolutional neural network and texture analysis
V Alex, M Safwan, G Krishnamurthi - … with MICCAI 2017, Quebec City, QC …, 2018 - Springer
V Alex, M Safwan, G Krishnamurthi
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018•SpringerIn 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 …
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 …
Abstract
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%.
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