Automatic quantification of brain midline shift in CT images

Abstract

Computer Tomography (CT) images of traumatic brain injury (TBI) are widely used for clinical diagnosis. Pathological features on these images such as the volume and type of hemorrhage regions, the amount of brain midline shift, and the volume of ventricle are important indicators based on which decision of treatment or prognosis is made. Among the various clinical features, brain midline shift (MLS) is a significant factor in TBI diagnosis, which is a major cause of death. It indicates the severity of injury and the chance of survival of patients. Many studies have been carried out to find the associations between MLS and the injury outcomes such as disability or mortality. However, in these studies, measurements of MLS are either quantitatively measured manually by experts or described qualitatively. Due to the lack of quantified data in large population, no precise or reliable statistical figures can be obtained. In addition, there may be many unknown associations to be discovered if large quantified datasets are available. Therefore, automatically quantifying the MLS in CT image has become an urgent task for TBI prognosis research. Once efficient quantifying methods are developed and applied to large brain image database, finding precise and reliable statistical figures and building fast and effective predictive models for TBI prognosis will become a much easier task. Techniques to be developed in this thesis will provide prognosis research in TBI with significantly rich amount of quantified image data, specifically, the quantified brain midline shift, which have never been available before to doctors and researchers. With the new methods and findings, new prototype online retrieval system is to be developed. It is hoped that outcomes from the present project will eventually benefit the traumatic brain injury clinical diagnosis, treatment, patients? survival and recovery.