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Objective: Hydrocephalus is a medical condition in which there is an abnormalaccumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brainimagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs.postop) plays a crucial role in evaluating surgical treatment. Segmentation ofpre-op images is often a relatively straightforward problem and has been wellresearched. However, segmenting post-operative (post-op) computationaltomographic (CT)-scans becomes more challenging due to distorted anatomy andsubdural hematoma collections pressing on the brain. Most intensity and featurebased segmentation methods fail to separate subdurals from brain and CSF assubdural geometry varies greatly across different patients and their intensityvaries with time. We combat this problem by a learning approach that treatssegmentation as supervised classification at the pixel level, i.e. a trainingset of CT scans with labeled pixel identities is employed. Methods: Ourcontributions include: 1.) a dictionary learning framework that learns class(segment) specific dictionaries that can efficiently represent test samplesfrom the same class while poorly represent corresponding samples from otherclasses, 2.) quantification of associated computation and memory footprint, and3.) a customized training and test procedure for segmenting post-ophydrocephalic CT images. Results: Experiments performed on infant CT brainimages acquired from the CURE Children's Hospital of Uganda reveal the successof our method against the state-of-the-art alternatives. We also demonstratethat the proposed algorithm is computationally less burdensome and exhibits agraceful degradation against number of training samples, enhancing itsdeployment potential.

Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans