Coordinated Sensor-Based Area Coverage and Cooperative Localization of a Heterogeneous Fleet of Autonomous Surface Vessels (ASVs)

Sensor coverage with fleets of robots is a complex task requiring solutions to localization, communication, navigation and basic sensor coverage. Sensor coverage of large areas is a problem that occurs in a variety of different environments from terrestrial to aerial to aquatic. In this thesis we consider the aquatic version of the problem. Given a known aquatic environment and collection of aquatic surface vehicles with known kinematic and dynamic constraints, how can a fleet of vehicles be deployed to provide sensor coverage of the surface of the body of water? Rather than considering this problem in general, in this work we consider the problem given a specific fleet consisting of one very well equipped robot aided by a number of smaller, less well equipped devices that must operate in close proximity to the main robot. A boustrophedon decomposition algorithm is developed that incorporates the motion, sensing and communication constraints imposed by the autonomous fleet. Solving the coverage problem leads to a localization/communication problem. A critical problem for a group of autonomous vehicles is ensuring that the collection operates within a common reference frame. Here we consider the problem of localizing a heterogenous collection of aquatic surface vessels within a global reference frame. We assume that one vessel -- the mother robot -- has access to global position data of high accuracy, while the other vessels -- the child robots -- utilize limited onboard sensors and sophisticated sensors on board the mother robot to localize themselves. This thesis provides details of the design of the elements of the heterogeneous fleet including the sensors and sensing algorithms along with the communication strategy used to localize all elements of the fleet within a global reference frame. Details of the robot platforms to be used in implementing a solution are also described. Simulation of the approach is used to demonstrate the effectiveness of the algorithm, and the algorithm and its components are evaluated using a fleet of ASVs

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI

Progressive white matter injury in preclinical dutch cerebral amyloid angiopathy

Autosomal-dominant, Dutch-type cerebral amyloid angiopathy (D-CAA) offers a unique opportunity to develop biomarkers for pre-symptomatic cerebral amyloid angiopathy (CAA). We hypothesized that neuroimaging measures of white matter injury would be present and progressive in D-CAA prior to hemorrhagic lesions or symptomatic hemorrhage. In a longitudinal cohort of D-CAA carriers and non-carriers, we observed divergence of white matter injury measures between D-CAA carriers and non-carriers prior to the appearance of cerebral microbleeds and >14 years before the average age of first symptomatic hemorrhage. These results indicate that white matter disruption measures may be valuable cross-sectional and longitudinal biomarkers of D-CAA progression

Progressive white matter injury in preclinical Dutch cerebral amyloid angiopathy

Autosomal-dominant, Dutch-type cerebral amyloid angiopathy (D-CAA) offers a unique opportunity to develop biomarkers for pre-symptomatic cerebral amyloid angiopathy (CAA). We hypothesized that neuroimaging measures of white matter injury would be present and progressive in D-CAA prior to hemorrhagic lesions or symptomatic hemorrhage. In a longitudinal cohort of D-CAA carriers and non-carriers, we observed divergence of white matter injury measures between D-CAA carriers and non-carriers prior to the appearance of cerebral microbleeds and \u3e 14 years before the average age of first symptomatic hemorrhage. These results indicate that white matter disruption measures may be valuable cross-sectional and longitudinal biomarkers of D-CAA progression. ANN NEUROL 2022;92:358–363