Left Renal Vein Abnormalities Detected During Routine Abdominal Computed Tomography Imaging: Clinico-radiological Significance

Purpose: Preoperative knowledge of the presence of major venous anomalies facilitates the safe performance of aortic surgery. The purpose of the study was to estimate the incidence, as detected by abdominal computed tomography (CT), of major left renal vein anomalies related to the abdominal aorta in an adult population. Materials and methods: Seven hundred and fifty abdominal CT examinations were reviewed retrospectively for the presence of left renal vein anomalies. Eleven CT scans were excluded from the study because of technical or patient-related factors. The course of the left renal vein was assessed on the CT slices to detect any anomalies. Results: Left renal vein anomaly was detected in 23 (3.1%) of 739 cases. Seventeen (2.3%) of them were a retro-aortic and six (0.8%) of them were a circum-aortic left renal vein. Conclusion: It is important to detect left renal vein anomalies before retroperitoneal surgery or interventional procedures. These anomalies can be identified in routine abdominal CT examinations with a careful inspection

Assessment of dimensions of pneumatisation of the anterior clinoid process in middle Anatolian population by computed tomography

Background: The anterior clinoid process (ACP) is usually removed during surgical procedures of the cellar region. The ACP may be different length and width in people; it may be also pneumatic. Therefore, we aimed to determine dimensions and rates of pneumatisation of the ACP in the large study group with clinicallyimportance.Materials and methods: One thousand and thirty-one (592 female, 439 male) cranial computed tomography (CT) of the middle Anatolian population was used in this study. The length and basal width of the ACP were measured on the cranial CT. Also; incidence and degree of ACP pneumatisation were identified.Results: The width of the right and left ACPs in females were 10.80 ± 2.27 mm and 10.53 ± 2.07 mm, respectively. The width of the right and left ACPs in males were 11.08 ± 2.39 mm and 10.98 ± 2.35 mm, respectively. The length of the right and left ACPs in females were 8.32 ± 2.40 mm and 8.34 ± 2.35 mm, respectively. The length of the right and left ACPs in males were 8.87 ± 2.62 mm and 8.93 ± 2.64 mm, respectively. There was statistically significant difference between males and females in ACP dimensions, except for the width of the right ACP. Pneumatisation of the ACP was observed on the right side in 46 (9.3%) cases,on the left side in 53 (10.6%) cases, and bilaterally in 32 (6.5%) cases. Incidence of pneumatisation of the ACP was decreased in the age group of 1 month to 20 years. While the incidence of bilateral pneumatisation of the ACP was higher in individuals aged 21–40.Conclusions: Radiologically recognising pneumatisation and anatomical variations of the ACP may be helpful in decreasing the incidence of surgical complications during anterior clinoidectomy

Cropland Capture – A Game for Improving Global Cropland Maps

Current satellite-derived global land-cover products, which are crucial for many modelling and monitoring applications, show large disagreements when compared with each another. To help improve global land cover (in particular the cropland class), we developed a game called Cropland Capture. This is a simple cross-platform game for collecting image classifications that will be used to develop and validate global cropland maps in the future. In this paper, we describe the game design of Cropland Capture in detail, including aspects such as simplicity,efficiency in data collection and what mechanisms were implemented to ensure data quality.We also discuss the impact of incentives on attracting and sustaining players in the game

Improved Vote Aggregation Techniques for the Geo-Wiki Cropland Capture Crowdsourcing Game

Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth’s surface. More recently, the Picture Pile game, which is a more generalized version of Cropland Capture, aims to identify tree loss over time from pairs of very high resolution satellite images. Despite recent progress in image analysis, the solution to these problems is hard to automate since human experts still outperform the majority of machine learning algorithms and artificial systems in this field on certain image recognition tasks. The replacement of rare and expensive experts by a team of distributed volunteers seems to be promising, but this approach leads to challenging questions such as: how can individual opinions be aggregated optimally, how can confidence bounds be obtained, and how can the unreliability of volunteers be dealt with? In this paper, on the basis of several known machine learning techniques, we propose a technical approach to improve the overall performance of the majority voting decision rule used in the Cropland Capture Game. The proposed approach increases the estimated consistency with expert opinion from 77% to 86%

How to Increase the Accuracy of Crowdsourcing Campaigns?

Crowdsourcing is a new approach to performing tasks, with a group of volunteers rather than experts. For example, the Geo-Wiki project [1] aims to improve the global land-cover map by crowdsourcing for image recognition. Though crowdsourcing gives a simple way to perform tasks that are hard to automate, analysis of data received from non-experts is a challenging problem that requires a holistic approach. Here we study in detail the dataset of the Cropland Capture game (part of Geo-Wiki project) to increase the accuracy of campaign’s results. Using this analysis, we developed a methodology for a generic type of crowdsourcing campaign similar to the Cropland Capture game. The proposed methodology relies on computer vision and machine learning techniques. Using the Cropland Capture dataset we showed that our methodology increases agreement between aggregated volunteers’ votes and experts’ decisions from 77% to 86%. [1] Fritz, Steffen, et al. “Geo-Wiki. Org: The use of crowdsourcing to improve global land cover.” Remote Sensing 1.3 (2009): 345-354

Effects of Electron Correlations on Hofstadter Spectrum

By allowing interactions between electrons, a new Harper's equation is derived to examine the effects of electron correlations on the Hofstadter energy spectra. It is shown that the structure of the Hofstadter butterfly ofr the system of correlated electrons is modified only in the band gaps and the band widths, but not in the characteristics of self-similarity and the Cantor set.Comment: 13 pages, 5 Postscript figure