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

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Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithmso this disruption. Even so, traumatic injury often disrupts brain morphology as well, complicating the analysis of brain integrity and connectivity, which are typically evaluated with tractography methods optimized for analyzing normal healthy brains. To understand which fiber tracking methods show

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Combining Unsupervised and Supervised Methods for Lesion Segmentations. We present a novel automated lesion segmentation method consisting of an unsupervised mixture model based extraction of candidate lesion voxels, which are subsequently classified by a random decision forest (RDF) using simple visual features like multi-sequence MR intensities sourced from connect

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A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequencesor multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the d

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Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Imagesker for stroke recovery. This measure is highly sensitive to applied pre-processing steps; in particular, the presence of a lesion may result into severe misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduc

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Image Features for Brain Lesion Segmentation Using Random Forestsin MR scans. This paper describes a set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task. Embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke (ISLES 2015 - SISS), acute ischemic stroke (ISLES 2015

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Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRIs for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Glioma

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GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modela hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification sc