Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; First International Alessandro Crimi,Bjoern Menze,Heinz Hand

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Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumorspairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show t

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Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders segmentation is time consuming, an automated method can be useful, especially in large clinical studies. Since Gliomas have variable shape and texture, automated segmentation is a challenging task and a number of techniques based on machine learning algorithms have been proposed. In the recent past

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A Convolutional Neural Network Approach to Brain Tumor Segmentationlesion. We propose a Convolutional Neural Network (CNN) approach which is amongst the top performing methods while also being extremely computationally efficient, a balance that existing methods have struggled to achieve. Our CNN is trained directly on the image modalities and thus learns a feature

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