  Collateral circulation results from specialized anastomotic channels which
are capable of providing oxygenated blood to regions with compromised blood
flow caused by ischemic injuries. The quality of collateral circulation has
been established as a key factor in determining the likelihood of a favorable
clinical outcome and goes a long way to determine the choice of stroke care
model - that is the decision to transport or treat eligible patients
immediately.
  Though there exist several imaging methods and grading criteria for
quantifying collateral blood flow, the actual grading is mostly done through
manual inspection of the acquired images. This approach is associated with a
number of challenges. First, it is time-consuming - the clinician needs to scan
through several slices of images to ascertain the region of interest before
deciding on what severity grade to assign to a patient. Second, there is a high
tendency for bias and inconsistency in the final grade assigned to a patient
depending on the experience level of the clinician.
  We present a deep learning approach to predicting collateral flow grading in
stroke patients based on radiomic features extracted from MR perfusion data.
First, we formulate a region of interest detection task as a reinforcement
learning problem and train a deep learning network to automatically detect the
occluded region within the 3D MR perfusion volumes. Second, we extract radiomic
features from the obtained region of interest through local image descriptors
and denoising auto-encoders. Finally, we apply a convolutional neural network
and other machine learning classifiers to the extracted radiomic features to
automatically predict the collateral flow grading of the given patient volume
as one of three severity classes - no flow (0), moderate flow (1), and good
flow (2)...
