Automated brain segmentation methods for clinical quality MRI and CT images

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with brain tissue loss. Accurate estimation of this loss is critical for the diagnosis, prognosis, and tracking the progression of AD. Structural magnetic resonance imaging (sMRI) and X-ray computed tomography (CT) are widely used imaging modalities that help to in vivo map brain tissue distributions. As manual image segmentations are tedious and time-consuming, automated segmentation methods are increasingly applied to head MRI and head CT images to estimate brain tissue volumes. However, existing automated methods can be applied only to images that have high spatial resolution and their accuracy on heterogeneous low-quality clinical images has not been tested. Further, automated brain tissue segmentation methods for CT are not available, although CT is more widely acquired than MRI in the clinical setting. For these reasons, large clinical imaging archives are unusable for research studies. In this work, we identify and develop automated tissue segmentation and brain volumetry methods that can be applied to clinical quality MRI and CT images. In the first project, we surveyed the current MRI methods and validated the accuracy of these methods when applied to clinical quality images. We then developed CTSeg, a tissue segmentation method for CT images, by adopting the MRI technique that exhibited the highest reliability. CTSeg is an atlas-based statistical modeling method that relies on hand-curated features and cannot be applied to images of subjects with different diseases and age groups. Advanced deep learning-based segmentation methods use hierarchical representations and learn complex features in a data-driven manner. In our final project, we develop a fully automated deep learning segmentation method that uses contextual information to segment clinical quality head CT images. The application of this method on an AD dataset revealed larger differences between brain volumes of AD and control subjects. This dissertation demonstrates the potential of applying automated methods to large clinical imaging archives to answer research questions in a variety of studies

Ultrasound Guided Robot for Human Liver Biopsy using High Intensity Focused Ultrasound for Hemostasis

Percutaneous liver biopsy is the gold standard among clinician\u27s tool to diagnose and guide subsequent therapy for liver disease. Ultrasound image guidance is being increasingly used to reduce associated procedural risks but post–biopsy complications still persist. The major and most common complication is hemorrhage, which is highly unpredictable and may sometimes lead to death. Though the risk of mortality is low, it is too high for a diagnostic procedure. Post-biopsy care and additional surgical intervention to arrest hemorrhage make liver biopsy a costly procedure for health care delivery systems. Non-invasive methods to stop bleeding exist like electro–cautery, microwave, lasers, radio frequency, argon–beam, and High Intensity Focused Ultrasound (HIFU). All the methods except HIFU require direct exposure of the needle puncture site for hemostasis. HIFU is an ultrasound modality and uses mechanical sound waves for focused energy delivery. Ultrasound waves are minimally affected by tissue attenuation and focus internal targets without direct exposure. Human error in focusing HIFU renders it unusable for a medical procedure especially when noninvasive. In this project we designed and developed an ultrasound guided prototype robot for accurate HIFU targeting to induce hemostasis. The robotic system performs percutaneous needle biopsy and a 7.5 cm focal length HIFU is fired at the puncture point when the needle tip retracts to the liver surface after sample collection. The robot has 4 degrees of freedom (DOF) for biopsy needle insertion, HIFU positioning, needle angle alignment and US probe image plane orientation. As the needle puncture point is always in the needle path, mechanically constraining the HIFU to focus on the needle reduced the required functionality significantly. Two mini c-arms are designed for needle angle alignment and US probe image plane orientation. This reduced the contact foot print of the robot over the patient providing a greater dexterity for positioning the robot. The robot is validated for HIFU hemostasis by a series of experiments on chicken breasts. HIFU initiated hemorrhage control with robotic biopsy ensures arrest of post-biopsy hemorrhage and decreases patient anxiety, hospital stay, morbidity, time of procedure, and cost. This can also be extended to other organs like kidneys, lungs etc. and has widespread implications such as control of hemorrhage in post-biopsies in patients with reduced ability for hemostasis. This research opens a greater scope for research for automation and design making it a physician friendly tool for eventual clinical use

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach

Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying spatially heterogeneous effects of alcohol. In this study, adolescents (n = 657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study who endorsed little to no alcohol use at baseline were assessed with structural magnetic resonance imaging and followed longitudinally at four yearly intervals. Seven unique spatial patterns of covarying cortical thickness were obtained from the baseline scans by applying an unsupervised machine learning method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants' longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. We found in six NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts. Among moderate drinkers the decline was faster in the younger adolescent cohort and slower in the older cohort. Among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort. The findings suggested that unsupervised machine learning successfully delineated spatially coordinated patterns of vertex-level cortical thickness variation that are unconstrained by neuroanatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers, an effect that is strongest among heavy drinkers