A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Minimal local anesthetic volume for peripheral nerve block: a new ultrasound-guided, nerve dimension-based method

BACKGROUND AND OBJECTIVES: Nerve blocks using local anesthetics are widely used. High volumes are usually injected, which may predispose patients to associated adverse events. Introduction of ultrasound guidance facilitates the reduction of volume, but the minimal effective volume is unknown. In this study, we estimated the 50% effective dose (ED50) and 95% effective dose (ED95) volume of 1% mepivacaine relative to the cross-sectional area of the nerve for an adequate sensory block. METHODS: To reduce the number of healthy volunteers, we used a volume reduction protocol using the up-and-down procedure according to the Dixon average method. The ulnar nerve was scanned at the proximal forearm, and the cross-sectional area was measured by ultrasound. In the first volunteer, a volume of 0.4 mL/mm of nerve cross-sectional area was injected under ultrasound guidance in close proximity to and around the nerve using a multiple injection technique. The volume in the next volunteer was reduced by 0.04 mL/mm in case of complete blockade and augmented by the same amount in case of incomplete sensory blockade within 20 mins. After 3 up-and-down cycles, ED50 and ED95 were estimated. Volunteers and physicians performing the block were blinded to the volume used. RESULTS: A total 17 of volunteers were investigated. The ED50 volume was 0.08 mL/mm (SD, 0.01 mL/mm), and the ED95 volume was 0.11 mL/mm (SD, 0.03 mL/mm). The mean cross-sectional area of the nerves was 6.2 mm (1.0 mm). CONCLUSIONS: Based on the ultrasound measured cross-sectional area and using ultrasound guidance, a mean volume of 0.7 mL represents the ED95 dose of 1% mepivacaine to block the ulnar nerve at the proximal forearm.

Precipitation over the past four centuries in the Dieshan Mountains as inferred from tree rings: An introduction to an HHT-based method

To improve our understanding of the Asian monsoon system, we developed a hydroclimate reconstruction in a marginal monsoon shoulder region for the period prior to the industrial era. Here, we present the first moisture sensitive tree-ring chronology, spanning 501 years for the Dieshan Mountain area, a boundary region of the Asian summer monsoon in the northeastern Tibetan Plateau. This reconstruction was derived from 101 cores of 68 old-growth Chinese pine (Pinus tabulaeformis) trees. We introduce a Hilbert–Huang Transform (HHT) based standardization method to develop the tree-ring chronology, which has the advantages of excluding non-climatic disturbances in individual tree-ring series. Based on the reliable portion of the chronology, we reconstructed the annual (prior July to current June) precipitation history since 1637 for the Dieshan Mountain area and were able to explain 41.3% of the variance. The extremely dry years in this reconstruction were also found in historical documents and are also associated with El Niño episodes. Dry periods were reconstructed for 1718–1725, 1766–1770 and 1920–1933, whereas 1782–1788 and 1979–1985 were wet periods. The spatial signatures of these events were supported by data from other marginal regions of the Asian summer monsoon. Over the past four centuries, out-of-phase relationships between hydroclimate variations in the Dieshan Mountain area and far western Mongolia were observed during the 1718–1725 and 1766–1770 dry periods and the 1979–1985 wet period.

An image-based method to automatically propagate bony landmarks: application to computational spine biomechanics.

In attempts to elucidate the underlying mechanisms of spinal injuries and spinal deformities, several experimental and numerical studies have been conducted to understand the biomechanical behavior of the spine. However, numerical biomechanical studies suffer from uncertainties associated with hard- and soft-tissue anatomies. Currently, these parameters are identified manually on each mesh model prior to simulations. The determination of soft connective tissues on finite element meshes can be a tedious procedure, which limits the number of models used in the numerical studies to a few instances. In order to address these limitations, an image-based method for automatic morphing of soft connective tissues has been proposed. Results showed that the proposed method is capable to accurately determine the spatial locations of predetermined bony landmarks. The present method can be used to automatically generate patient-specific models, which may be helpful in designing studies involving a large number of instances and to understand the mechanical behavior of biomechanical structures across a given population.

Assessing distractors and teamwork during surgery: developing an event-based method for direct observation

OBJECTIVE: To develop a behavioural observation method to simultaneously assess distractors and communication/teamwork during surgical procedures through direct, on-site observations; to establish the reliability of the method for long (>3 h) procedures. METHODS: Observational categories for an event-based coding system were developed based on expert interviews, observations and a literature review. Using Cohen's κ and the intraclass correlation coefficient, interobserver agreement was assessed for 29 procedures. Agreement was calculated for the entire surgery, and for the 1st hour. In addition, interobserver agreement was assessed between two tired observers and between a tired and a non-tired observer after 3 h of surgery. RESULTS: The observational system has five codes for distractors (door openings, noise distractors, technical distractors, side conversations and interruptions), eight codes for communication/teamwork (case-relevant communication, teaching, leadership, problem solving, case-irrelevant communication, laughter, tension and communication with external visitors) and five contextual codes (incision, last stitch, personnel changes in the sterile team, location changes around the table and incidents). Based on 5-min intervals, Cohen's κ was good to excellent for distractors (0.74-0.98) and for communication/teamwork (0.70-1). Based on frequency counts, intraclass correlation coefficient was excellent for distractors (0.86-0.99) and good to excellent for communication/teamwork (0.45-0.99). After 3 h of surgery, Cohen's κ was 0.78-0.93 for distractors, and 0.79-1 for communication/teamwork. DISCUSSION: The observational method developed allows a single observer to simultaneously assess distractors and communication/teamwork. Even for long procedures, high interobserver agreement can be achieved. Data collected with this method allow for investigating separate or combined effects of distractions and communication/teamwork on surgical performance and patient outcomes.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.