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

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https://doi.org/10.1057/9780230590182assification on entire volume data, which requires heavy load of both computation and memory, we propose a two-stage approach. We first normalize image intensity and segment the whole tumor by utilizing the anatomy structure information. By dilating the initial segmented tumor as the region of inter

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978-3-319-55523-2Springer International Publishing AG 2016

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Alessandro Crimi,Bjoern Menze,Heinz HandelsIncludes supplementary material:

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Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentationa fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

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3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Archt architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.

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Economic Fluctuation and Stabilizationerfusion parameters and a nosologic segmentation map of the vascular habitats of the GBM. A radiologic report summarizes the findings of both analysis and provides volumetric and perfusion statistics of each tissue and habitat of the tumour.