An automatic CT pipeline combining a spatially aware neural network, tensor-based graph cuts, and Voronoi diagrams estimates renal vascular dominant regions with 80% Dice on 8 cases.
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
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abstract
Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.
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Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
An automatic CT pipeline combining a spatially aware neural network, tensor-based graph cuts, and Voronoi diagrams estimates renal vascular dominant regions with 80% Dice on 8 cases.