  Semantic image segmentation plays an important role in modeling
patient-specific anatomy. We propose a convolution neural network, called
Kid-Net, along with a training schema to segment kidney vessels: artery, vein
and collecting system. Such segmentation is vital during the surgical planning
phase in which medical decisions are made before surgical incision. Our main
contribution is developing a training schema that handles unbalanced data,
reduces false positives and enables high-resolution segmentation with a limited
memory budget. These objectives are attained using dynamic weighting, random
sampling and 3D patch segmentation. Manual medical image annotation is both
time-consuming and expensive. Kid-Net reduces kidney vessels segmentation time
from matter of hours to minutes. It is trained end-to-end using 3D patches from
volumetric CT-images. A complete segmentation for a 512x512x512 CT-volume is
obtained within a few minutes (1-2 mins) by stitching the output 3D patches
together. Feature down-sampling and up-sampling are utilized to achieve higher
classification and localization accuracies. Quantitative and qualitative
evaluation results on a challenging testing dataset show Kid-Net competence.
