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

An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producer

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 gaming approach to improve global land cover

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Accurate and reliable information on global cropland extent is needed for a number of applications, e g. to estimate potential yield losses in the wake of a drought or for assessing future scenarios of climate change on crop production. However, current global land cover and cropland products are not accurate enough for many of these applications. One way forward is to increase the amount of data that are used to create these maps as well as for validation purposes. One method for doing this is to involve citizens in the classification of satellite imagery as undertaken using the Geo-Wiki tool. This paper outlines Cropland Capture, which is simplified game version of Geo-Wiki in which players classify satellite imagery based on whether they can see evidence of cropland or not. On overview of the game is provided along with some initial results from the first 3 months of game play. The paper concludes with a discussion of the future steps in this research

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement

Comparing the quality of crowdsourced data contributed by expert and non-experts

There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but here are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future

How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels

Lateralization of infant holding by mothers: a longitudinal evaluation of variations over the first 12 weeks

The maternal preference to hold infants on the left rather than right side of the body was examined longitudinally, with attention to four explanations: maternal monitoring of infant state, maternal handedness, infant proximity to the mother’s heartbeat, and preferred infant head position. The side and site of holding were measured over the first twelve weeks of the lives of 24 infants. Information about group and individual consistency in holding side allowed novel evaluation of the theories. A strong bias to hold on the left dropped below significance when the infants were aged twelve weeks and was limited to specific holding positions. Findings were generally consistent with the monitoring hypothesis, and little support was found for the three alternative explanations