  Objective: Hydrocephalus is a medical condition in which there is an abnormal
accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain
imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs.
postop) plays a crucial role in evaluating surgical treatment. Segmentation of
pre-op images is often a relatively straightforward problem and has been well
researched. However, segmenting post-operative (post-op) computational
tomographic (CT)-scans becomes more challenging due to distorted anatomy and
subdural hematoma collections pressing on the brain. Most intensity and feature
based segmentation methods fail to separate subdurals from brain and CSF as
subdural geometry varies greatly across different patients and their intensity
varies with time. We combat this problem by a learning approach that treats
segmentation as supervised classification at the pixel level, i.e. a training
set of CT scans with labeled pixel identities is employed. Methods: Our
contributions include: 1.) a dictionary learning framework that learns class
(segment) specific dictionaries that can efficiently represent test samples
from the same class while poorly represent corresponding samples from other
classes, 2.) quantification of associated computation and memory footprint, and
3.) a customized training and test procedure for segmenting post-op
hydrocephalic CT images. Results: Experiments performed on infant CT brain
images acquired from the CURE Children's Hospital of Uganda reveal the success
of our method against the state-of-the-art alternatives. We also demonstrate
that the proposed algorithm is computationally less burdensome and exhibits a
graceful degradation against number of training samples, enhancing its
deployment potential.
