Bento 4 adds two new forms templates that, according to the company, offer enhanced printing capabilities. These forms, one called Etched and the other Classic, each offer a simple look and feel that limit the number of colors used when printing and, when printing, the Bento print dialog allows you to hide field labels and borders. But, while form printing in Bento has improved, your printed output still tend to look more like a screenshot than a business-quality form.
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These printing routines give you a lot of control on how the label is organized. There is a print preview, so you can see how the labels will look. There is also the ability to start printing on some label other than number one. This is great if you have a partially used sheet of labels and want to finish it up.
I am a big fan of the iPod Touch and tend to use it as a PDA as more then a music player. I have been using Bento on the iPod touch for a number of tasks, but the most important use is for shopping lists. Rather than wasting a scrap of paper, I created a database of products at various stores. Along with this list, I created a smart list that shows only the items that I designated when needed (using a check box). At the store, I load the smart list, and as I put items in the cart, click off the check box, which removes the item from the list. Really cool and efficient! In the previous version of Bento for the iPod Touch, I had to select the item, open the record, to be able to click off the needed check box. In Version 4, you can choose to have a check box field shown on the list view. This means I can click off items directly from the list view, without opening the record. I love it!
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I just tried to open an app from iWork'09 folder on my Mac and it asked to buy it or enter the serial number. To clarify, I bought my Mac after 2013 Oct.1 and signed up for free download of the iWork 2013 update, so it's available for me at the moment to update further iterations for free, but how about iWork'09?
A well published pirating scheme, easily found in a web search, is that, if, for whatever reason, someone has a Mac that has the iWork '09 trial version installed on it and they update to OS X Mavericks 10.9, the Mavericks versions of the iWork apps will be installed. As with the paid versions of iWork '09, the trial iWork '09 apps will be sequestered in a folder titled iWork '09. They will still continue to be a trial version and will only function as a trial version unless someone has a previously purchased serial number to unlock the apps. AFAIK Apple has not sold the serial numbers to unlock the trial for a few years.
Objective: This retrospective study investigated abnormal hepatic dysfunction and abdominal computed tomography (CT) findings in scrub typhus. Methods: Three hundred forty nine adult patients were diagnosed with scrub typhus. Ninety four underwent abdominal CT. The CT images were reviewed by the attending radiologist. Patient data of history, symptoms, signs, and results of laboratory tests were collected from the electronic medical records. Results: In 349 patients with scrub typhus, elevation of aspartate aminotransferase (78.5%) and alanine aminotransferase (63.0%) were dominant compared to alkaline phosphatase (27.2%) and total bilirubin (16.1%). Abdominal CT findings of 94 patients were, in descending order of frequency, enlarged lymph node (53.2%), inhomogeneous enhancement of liver (47.9%), splenomegaly (46.8%), ascites (28.7%), low attenuation of periportal areas (27.7%), gallbladder wall thickening (17.0%), and splenic infarct (6.4%). Also, the level of aspartate aminotransferase tended to be elevated according to the number of CT findings (P= 0.028) Conclusions: We found that abdominal CT manifestations of scrub typhus with elevated aminotransferases were varied and not specific. However, knowledge of these findings may evoke the recognition of scrub typhus by clinicians in endemic areas. PMID:26101478
The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.
Computed tomography (CT) exams were conducted to determine the distribution of abdominal fat identified based on the CT number measured in Hounsfield Units (HU) and to measure the volume of the abdominal visceral and subcutaneous fat in minipigs. The relationship between the CT-based fat volumes of several vertebral levels and the entire abdomen and anthropometric data including the sagittal abdominal diameter and waist circumference were evaluated. Moreover, the total fat volumes at the T11, T13, L3, and L5 levels were compared with the total fat volume of the entire abdomen to define the landmark of abdominal fat distribution. Using a single-detector CT, six 6-month-old male minipigs were scanned under general anesthesia. Three radiologists then assessed the HU value of visceral and subcutaneous abdominal fat by drawing the region of interest manually at the T11, T13, L1, L3, and L5 levels. The CT number and abdominal fat determined in this way by the three radiologists was found to be correlated (intra-class coefficient = 0.9). The overall HU ranges for the visceral and subcutaneous fat depots were -147.47 to -83.46 and -131.62 to -90.97, respectively. The total fat volume of the entire abdomen was highly correlated with the volume of abdominal fat at the T13 level (r = 0.97, p
We present a technique to annotate multiple organs shown in 2-D abdominal/pelvic CT images using CBIR. This annotation task is motivated by our research interests in visual question-answering (VQA). We aim to apply results from this effort in Open-iSM, a multimodal biomedical search engine developed by the National Library of Medicine (NLM). Understanding visual content of biomedical images is a necessary step for VQA. Though sufficient annotational information about an image may be available in related textual metadata, not all may be useful as descriptive tags, particularly for anatomy on the image. In this paper, we develop and evaluate a multi-label image annotation method using CBIR. We evaluate our method on two 2-D CT image datasets we generated from 3-D volumetric data obtained from a multi-organ segmentation challenge hosted in MICCAI 2015. Shape and spatial layout information is used to encode visual characteristics of the anatomy. We adapt a weighted voting scheme to assign multiple labels to the query image by combining the labels of the images identified as similar by the method. Key parameters that may affect the annotation performance, such as the number of images used in the label voting and the threshold for excluding labels that have low weights, are studied. The method proposes a coarse-to-fine retrieval strategy which integrates the classification with the nearest-neighbor search. Results from our evaluation (using the MICCAI CT image datasets as well as figures from Open-i) are presented.
To assess the effect of noise reduction filters on detection and characterization of lesions on low-radiation-dose abdominal computed tomographic (CT) images. Low-dose CT images of abdominal lesions in 19 consecutive patients (11 women, eight men; age range, 32-78 years) were obtained at reduced tube currents (120-144 mAs). These baseline low-dose CT images were postprocessed with six noise reduction filters; the resulting postprocessed images were then randomly assorted with baseline images. Three radiologists performed independent evaluation of randomized images for presence, number, margins, attenuation, conspicuity, calcification, and enhancement of lesions, as well as image noise. Side-by-side comparison of baseline images with postprocessed images was performed by using a five-point scale for assessing lesion conspicuity and margins, image noise, beam hardening, and diagnostic acceptability. Quantitative noise and contrast-to-noise ratio were obtained for all liver lesions. Statistical analysis was performed by using the Wilcoxon signed rank test, Student t test, and kappa test of agreement. Significant reduction of noise was observed in images postprocessed with filter F compared with the noise in baseline nonfiltered images (P =.004). Although the number of lesions seen on baseline images and that seen on postprocessed images were identical, lesions were less conspicuous on postprocessed images than on baseline images. A decrease in quantitative image noise and contrast-to-noise ratio for liver lesions was noted with all noise reduction filters. There was good interobserver agreement (kappa = 0.7). Although the use of currently available noise reduction filters improves image noise and ameliorates beam-hardening artifacts at low-dose CT, such filters are limited by a compromise in lesion conspicuity and appearance in comparison with lesion conspicuity and appearance on baseline low-dose CT images. Copyright RSNA, 2004 2ff7e9595c
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