Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate 7 statistical and voting-based label fusion algorithms (and 6 additional variants) to segment the optic nerves, eye globes and chiasm. For non-local STAPLE, we evaluate different intensity similarity measures (including mean square difference, locally normalized cross correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning based correction method for consistent bias correction and Markov random field regularization. The multi-atlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, NLSS with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm and 2.15 mm), but slightly worse on the globes. The fully automated multi-atlas labeling approach provides robust segmentations of orbital structures on MRI even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.
The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Ex vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.
An emerging trend in Internet of Things (IoT) applications is to move the computation (cyber) closer to the source of the data (physical). This paradigm is often referred to as edge computing . If edge resources are pooled together, they can be used as decentralized shared resources for IoT applications, providing increased capacity to scale up computations and minimize end-to-end latency. Managing applications on these edge resources is hard, however, due to their remote, distributed, and (possibly) dynamic nature, which necessitates autonomous management mechanisms that facilitate application deployment, failure avoidance, failure management, and incremental updates. To address these needs, we present CHARIOT, which is orchestration middleware capable of autonomously managing IoT systems consisting of edge resources and applications. CHARIOT implements a three-layer architecture. The topmost layer comprises a system description language, the middle layer comprises a persistent data storage layer and the corresponding schema to store system information, and the bottom layer comprises a management engine that uses information stored persistently to formulate constraints that encode system properties and requirements, thereby enabling the use of satisfiability modulo theory solvers to compute optimal system (re)configurations dynamically at runtime. This article describes the structure and functionality of CHARIOT and evaluates its efficacy as the basis for a smart parking system case study that uses sensors to manage parking spaces.
Industrial Internet of ings (IIoT) applications found in domains such as smart-grids, intelligent tranportation, manufacturing and healthcare systems, are distributed and mission-critical in nature. IIoT requires a scalable data sharing and dissemination platform that supports qulaity of service properties such as timeliness, resilience, and security. Although the Object Management Group (OMG)'s Data Distribution Service (DDS), which is a data-centric, peer-to-peer publish/subscribe standard supporting multiple QoS properties, is well-suited to meet the requirements of IIoT applications, the design of OMG DDS and current technology limitations constrains its use to local area networks only. Moreover, even though broker-based bridging services exist to interconnect isolated DDS networks, these solutions lack autonomous and dynamic coordination and discovery capabilities that are needed to bridge multiple, isolated networks on demand. To address these limitations, and enable a practical and readily deployable solution for IIoT, this paper presents PubSubCoord, which is an autonomous, coordination and discovery service for DDS endpoints operating over wide area networks (WANs). Empirical results evaluating the feasibility and performance of PubSubCoord are presented for (1) scalability of data dissemination and coordination, and (2) deadlineaware overlays employing con gurable QoS to provide low-latency data delivery for topics demanding strict service requirements. CCS CONCEPTS •Computer systems organization →Cloud computing; Peerto-peer architectures; •Applied computing →Event-driven architectures;
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