Human Cranium, Brain Ventricle and Blood Detection Using Machine Learning on Ultrasound Data

Abstract

Any head related injury can be very serious and may be classified as a traumatic brain injury (TBI), which can be a result of intracranial hemorrhaging. TBI is one of the most common injuries in or around a battlefield, which can be caused by both direct and indirect impacts. While assessing a brain injury in a well-equipped hospital is typically a trivial task, the same cannot be said about a TBI assessment in a non-hospital environment. Typically, a computer tomography (CT) machine is used to diagnose TBI. However, this project demonstrates the use of ultrasound and how it can be used to predict where skull, ventricles, and bleeding occur. The Pulsatility Research Group at the University of Washington has conducted three years of data collection and research to create a procedure that diagnoses TBI in a field situation. In this paper, machine learning methodologies will be used to predict these CT derived features. The result of this research shows that with adequate data and collection methods skull, ventricles, and potentially blood can be detected while applying machine learning to ultrasound obtained data.