  In this paper, we propose an automatic brain tumor segmentation approach
(e.g., PixelNet) using a pixel-level convolutional neural network (CNN). The
model extracts feature from multiple convolutional layers and concatenate them
to form a hyper-column where samples a modest number of pixels for
optimization. Hyper-column ensures both local and global contextual information
for pixel-wise predictors. The model confirms the statistical efficiency by
sampling a few pixels in the training phase where spatial redundancy limits the
information learning among the neighboring pixels in conventional pixel-level
semantic segmentation approaches. Besides, label skewness in training data
leads the convolutional model often converge to certain classes which is a
common problem in the medical dataset. We deal with this problem by selecting
an equal number of pixels for all the classes in sampling time. The proposed
model has achieved promising results in brain tumor and ischemic stroke lesion
segmentation datasets.
