Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space - ABSTRACT Hydrocephalus is the buildup of cerebrospinal fluid (CSF) in the cavities (ventricles) deep within the brain. The most common treatment for hydrocephalus is CSF diversion via ventriculoperitoneal (VP) shunting. Over 30,000 VP shunts are placed per year in the United States by some estimates. Despite how commonly this surgery is performed, the complication rate has been estimated at almost 24%, with one report citing a 22% rate of revision. Nearly 50% of patients admitted with shunt related issues require a stay of five or more days. Given the rate of surgical site infections and complications associated with shunt explorations and revisions, accurate diagnosis of a shunt malfunction remains a critical, if elusive, goal for many neurosurgeons. One of the difficulties in establishing a diagnosis based on imaging alone is the lack of standardized robust methods of measuring ventricular size. Recently volumetric analyses have been studied as a method for measuring ventricular size, as compared to the Evans’ Index or frontal-occipital horn ratios and have been suggested is more accurate and a better tool for measuring response of ventricular size to shunting. However, the associated human efforts and inter- and intra-observer variability in segmenting the ventricles prohibits its wide clinical adoption. The other difficulty with establishing a diagnosis of ventriculomegaly or hydrocephalus, involves a lack of a standardized, normative dataset with a range of what is considered normal for various age ranges as the ventricle size increases with age. Current literature lacks a robust normative dataset of ventricular size by age and gender and only recently has such a dataset been produced for the pediatric age range. Establishment of normative values for ventricular volume and morphology across all age population is sorely needed and will allow for the investigation of a variety of topics related to hydrocephalus and ultimately assisting in the detection and triage of hydrocephalus and VP shunt related complications or malfunctions. In recent years, the rapid development of deep learning (DL) models has led to great impact on many areas of medicine, especially for automatic image analysis tasks including segmentation. Taking advantage of DL models, two aims are proposed in this project: 1) develop and validate a robust DL model for ventricle segmentation including multi-modality support and automatic failure detection and build a normative database; 2) develop a software prototype that incorporates the DL model and normative values and fits the clinical workflow for image-based diagnosis of shunt malfunction. Ultimately, a unique software product will be developed and commercialized to improve the diagnosis of shunt malfunction and hydrocephalus and benefit the patients with better surgical outcome and reduced cost.
