Deep Learning Methods to Identify Human Cranium, Brain Ventricles, and Intracranial Hemorrhage Using Tissue Pulsatility Ultrasound Imaging

Abstract

Traumatic Brain Injury (TBI) is a serious medical condition when a person experiences trauma in the head, resulting in intracranial hemorrhage (bleeding) and potential deformation of head-enclosed anatomical structures. Detecting these abnormalities early is the key to saving lives and improving survival outcomes. Standard methods of detecting intracranial hemorrhage are Computed Tomography (CT) and Magnetic Resonant Imaging (MRI). However, they are not readily available on the battlefield and in low-income settings. A team of researchers from the University of Washington developed a novel ultrasound signal processing technique called Tissue Pulsatility Imaging (TPI) that operates on raw ultrasound data collected using a hand-held tablet-like ultrasound device. This research work aims to build segmentation deep-learning models that take the input TPI data and detect the skull, ventricles, and intracranial hemorrhage in a patient's head. We employed the U-Net architecture and four of its variants for this purpose. Results show that the proposed methods can segment the brain-enclosing skull and is relatively successful in ventricle detection, while more work is needed to produce a model that can reliably segment intracranial hemorrhage.