Automated Glioma Segmentation in MRI using Deep Convolutional Networks

Sammanfattning: Manual segmentation of brain tumours is a time consuming process, results often show high variability, and there is a call for automation in clinical practice. In this thesis the use of deep convolutional networks for automatic glioma segmentation in MRI is investigated. The implemented networks are evaluated on data used in the brain tumor segmentation challenge (BraTS). It is found that 3D convolutional networks generally outperform 2D convolutional networks, and that the best networks can produce segmentations that closely resemble human segmentations. Convolutional networks are also evaluated as feature extractors with linear SVM classifiers on top, and although the sensitivity is improved considerably, the segmentations are heavily oversegmented. The importance of the amount of data available is investigated as well by comparing results from networks trained on both 2013 and the greatly extended 2014 data set, but it is found that the method of producing ground-truth was also a contributing factor. The networks does not beat the previous high-scores on the BraTS data, but several simple improvement areas are identified to take the networks further.