Automated segmentation of lateral ventricles in brain CT images

It is estimated that every year, 1.5 million people in the United States sustain a traumatic brain injury (TBI). Over 50,000 of these patients will not survive, and many others will be left permanently disabled. TBI is known to be accompanied by an increase in intracranial pressure (ICP), as the presence of hematomas compresses the brain tissue. Severe ICP can be fatal, and so must be monitored. This typically requires cranial trepanation, a risky procedure for the patient. However, some signs of increased ICP are visible on medical scans. For example, the lateral ventricles may change in size and position, depending on the location of the original injury. In this paper, we focus on automatic processing of CT brain images to segment and identify the lateral ventricles, using both iterated conditional models (ICM) and maximum a posteriori spatial probability (MASP). The ideal midline of the brain is found via exhaustive search based on skull symmetry and tissue features. The horizontal shift in the ventricles associated with increased ICP can then later be calculated based on the ideal midline. The novelty of the proposed method lies in its combination of anatomical features with template matching against MRI images, its stepwise improvement of the detected actual midline, and its comparison of two existing methods, ICM and MASP, for ventricle detection. The relatively large size of the CT dataset used for testing increases the reliability of the results.