Konstantinos Kamnitsas

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We present our 11-layers deep, double-pathway, 3D Convo-lutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases.(More)
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have(More)
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an(More)
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached(More)
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