We develop a deep learning approach for automated intracerebral hemorrhage (ICH) segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by integrating dilated convolution neural network (CNN) with hypercolumn features where a modest number of pixels are sampled and corresponding features from multiple layers are concatenated. Due to freedom of sampling pixels rather than image patch, this model trains within the brain region and ignores the CT background padding. This boosts the convergence time and accuracy by learning only healthy and defected brain tissues. To overcome the class imbalance problem, we sample an equal number of pixels from each class. We also incorporate 3D conditional random field (3D CRF) to smoothen the predicted segmentation as a post-processing step. ICHNet demonstrates 87.6% Dice accuracy in hemorrhage segmentation, that is comparable to radiologists.