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Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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There has been significant scientific research on this topic, particularly for eCommerce websites. When considering a purchase decision, a key challenge issue online is trust. How do you gauge if a particular website is trustworthy? Taking lessons from TV advertising and general marketing principles, you may think that associating photos of people with products engenders trust.
Not just scientific research, real-world A/B tests also corroborate the hypothesis of human photos increasing conversion rate. Two of our customers used our A/B testing tool (VWO) to test the presence of human photos and its impact on conversions. The following sections detail what they did and what they found out.
Another VWO user, Jason Thompson, conducted an A/B test on the contact section of his blog to see if replacing an icon with his own photo would lead to more people contacting him. Following is a screenshot of the original and variation:
People want to connect with other people emotionally, the photo makes that emotional connection so much easier and as the test is proving, drives people to the contact form more than a nondescript icon.
Objective. The purpose of this study is to assess its human images and its unique capabilities such as the 'on demand' higher spatial resolution and multi-spectral imaging of photon-counting-detector (PCD)-CT.Approach. In this study, the FDA 510(k) cleared mobile PCD-CT (OmniTom Elite) was used. To this end, we imaged internationally certified CT phantoms and a human cadaver head to evaluate the feasibility of high resolution (HR) and multi-energy imaging. We also demonstrate the performance of PCD-CT via first-in-human imaging by scanning three human volunteers.Main results. At the 5 mm slice thickness, routinely used in diagnostic head CT, the first human PCD-CT images were diagnostically equivalent to the EID-CT scanner. The HR acquisition mode of PCD-CT achieved a resolution of 11 line-pairs (lp)/cm as compared to 7 lp cm-1using the same kernel (posterior fossa-kernel) in the standard acquisition mode of EID-CT. For the quantitative multi-energy CT performance, the measured CT numbers in virtual mono-energetic images (VMI) of iodine inserts in the Gammex Multi-Energy CT phantom (model 1492, Sun Nuclear Corporation, USA) matched the manufacturer reference values with mean percent error of 3.25%. Multi-energy decomposition with PCD-CT demonstrated the separation and quantification of iodine, calcium, and water.Significance. PCD-CT can achieve multi-resolution acquisition modes without physically changing the CT detector. It can provide superior spatial resolution compared with the standard acquisition mode the conventional mobile EID-CT. Quantitative spectral capability of PCD-CT can provide accurate, simultaneous multi-energy images for material decomposition and VMI generation using a single exposure.
Incredible brain imagery is now possible with the help of Google's AI. A joint team of Google researchers and Harvard neuroscientists have reconstructed nearly every cell and all of its connections within a small volume of human brain tissue. This 3D mapping required a monumental 1.4 petabytes of data and provides an unprecedented view into the human brain, which could help researchers understand neurological disorders and answer fundamental questions about how the brain works. The team's findings include the discovery of mirror-image clusters of cells, "axon whorls," and intricate networks of axons that communicate with neurons.
You shall have no other gods beside Me. You shall not make for yourself a sculptured image, or any likeness of what is in the heavens above or on the earth below, or in the waters under the earth. (Exodus 20:3-4)
In the 16th century, the Shulchan Aruch expanded the ban on creating sculptures, adding prohibitions against forming any three dimensional image that could be worshipped, including images that stand out in bas-relief (such as friezes). However, the Shulchan Aruch differs from the Talmud in that it allows one to create two-dimensional paintings and images of the human body, as long as the entire body is not shown. (Yoreh Deah 141-142)
Today most traditional rabbinic authorities go by the ruling in the Shulchan Aruch, sanctioning depictions of the human body that are somehow incomplete. For example, a sculpted bust would be acceptable, but not a full human form; a drawing in which part of the body is obstructed by a piece of furniture or another person would also be acceptable.
Pronounced: TALL-mud, Origin: Hebrew, the set of teachings and commentaries on the Torah that form the basis for Jewish law. Comprised of the Mishnah and the Gemara, it contains the opinions of thousands of rabbis from different periods in Jewish history.
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Author Contributions K.N.K. designed and conducted the experiment and was first author on the paper. K.N.K. and T.N. analysed the data. R.J.P. provided mathematical ideas and assistance. J.L.G. provided guidance on all aspects of the project. All authors discussed the results and commented on the manuscript.
Recent functional magnetic resonance imaging (fMRI) studies have shown that, based on patterns of activity evoked by different categories of visual images, it is possible to deduce simple features in the visual scene, or to which category it belongs. Kay et al. take this approach a tantalizing step further. Their newly developed decoding method, based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas, can identify with high accuracy which specific natural image an observer saw, even for an image chosen at random from 1,000 distinct images. This prompts the thought that it may soon be possible to decode subjective perceptual experiences such as visual imagery and dreams, an idea previously restricted to the realm of science fiction.
I tried several images in which I asked generative fill prompt to add people (girls or boys or both doesnt matter). It added people but all the time it placed humans with distorted faces as if those were aliens.
@MAXIBI32173616eym2 this is still what I would consider a "young" AI model and will improve over time. Other companies have had over a year headstart with a greater (albeit somewhat illegal) source pool to learn from.
Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos.
This paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed.
71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively. 152ee80cbc
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