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Motivated by our Christian identity, CURE operates a global network of children’s hospitals that provides life-changing surgical care to children living with disabilities.

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CURE International is a global nonprofit network of children’s hospitals providing surgical care in a compassionate, gospel-centered environment. Services are provided at no cost to families because of the generosity of donors and partners like you.

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Access to Surgery

Learning based segmentation of CT brain images: application to post-operative hydrocephalic scans

Abstract:

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., preoperative (pre-op) versus postoperative (post-op)] 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-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 a number of training samples, enhancing its deployment potential.

Publication: IEEE Transactions on Biomedical Engineering
Publication Year: 2018
Authors: Cherukuri, V. , Ssenyonga, P. , Warf, B. C. , Kulkarni, A. V. , Monga, V. , Schiff, S. J.
Tags
Africa
cerebrospinal fluid
computerized tomography
hydrocephalus
neurosurgery
Postoperative