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The Exodus Project Group

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Melthucelha Smith
Melthucelha Smith

Fix Visible Body 3d Human Anatomy Atlas Pc Cracked

The team at Visible Body is excited to launch the newest version of our award-winning Human Anatomy Atlas app. Human Anatomy Atlas 2023 is the first version to include 2D illustrations and histology slides in addition to our vast, easily navigable library of 3D anatomy models. Human Anatomy Atlas 2023 features 126 full color histology slides and 100 illustrations to help you teach, learn, and explore human anatomy.

Fix Visible Body 3d Human Anatomy Atlas Pc Cracked

Before buying this version, see the newer Human Anatomy Atlas 2023! Get visual Flashcards and cross-platform mobile and web access.Human Anatomy Atlas 2023 gives you core anatomy reference content on your iPhone or iPad. Want to expand your library? Check out our in-app purchases for additional anatomy and physiology content!Human Anatomy Atlas 2023 includes core anatomy reference content! Get the 3D interactive visual content you need to learn about the human body:- Full female and male 3D models to study gross anatomy. View these alongside cadaver and diagnostic images.- 3D views of key organs at multiple levels. Study the lungs, bronchi, and alveoli; Review the kidneys, renal pyramids, and nephrons.- Muscle and bone models that you can move. Learn muscle actions, bone landmarks, attachments, innervations, and blood supply.- See how fascia divides the muscles of the upper and lower limbs into compartments.- Additional in-app purchases: Our video library allows you to explore and educate with more than 100 stunning patient education animations that cover key physiology and common pathologies, including cellular respiration, heart conduction, peristalsis, filtration, coronary artery disease, kidney stones, and sciatica.- Additional in-app purchase: 3D Dental Anatomy includes cusps, fossae, and surfaces, and cross-sectional views of an incisor, canine, premolar, double root molar, and triple root molar; plus, an interactive, animated model of the upper and lower arches.And so much more! All this content is organized so you can easily browse or search by topic and region.A full suite of study and presentation tools:- Dissect models on screen, in augmented reality (AR), and in cross-sections. Download free lab activities that walk you through key structures.- Take 3D dissection quizzes and check your progress.- Make interactive 3D presentations that link sets of models to explain and review a topic. Label structures with tags, notes, and 3D drawings.Share content with patients, classmates, students, and colleagues!

Free anatomy apps for medical students can help advance their understanding of the bodily structure of humans, animals and other living organisms from the convenience of their computer, tablet or phone. There are a number of these applications specifically designed for Macintosh computers, which are designed and marketed by Apple Inc. This article will share information about these free available anatomy applications, touching on how each is unique, what they offer learners and where they can be downloaded.

Anatomy 3D Atlas: As noted in the name of the app, this software allows you to manipulate a 3D version of the human body for a highly interactive experience that may be very effective for long-term retention of learning. Specifically designed for healthcare students, this app allows you to peel away layers of the human body, including muscles, organs and tissues all the way down to the skeletal structure.

The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

Human motion capture has a wide variety of applications, and in vision-based motion capture systems a major issue is the human body model and its initialization. We present a computer vision algorithm for building a human body model skeleton in an automatic way. The algorithm is based on the analysis of the human shape. We decompose the body into its main parts by computing the curvature of a B-spline parameterization of the human contour. This algorithm has been applied in a context where the user is standing in front of a camera stereo pair. The process is completed after the user assumes a predefined initial posture so as to identify the main joints and construct the human model. Using this model, the initialization problem of a vision-based markerless motion capture system of the human body is solved.

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy. The Author(s) 2014.

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

In a static monocular camera system, to gain a perfect 3D human body posture is a great challenge for Computer Vision technology now. This paper presented human postures recognition from video sequences using the Quantum-Inspired Immune Cloning Algorithm (QICA). The algorithm included three parts. Firstly, prior knowledge of human beings was used, the key joint points of human could be detected automatically from the human contours and skeletons which could be thinning from the contours; And due to the complexity of human movement, a forecasting mechanism of occlusion joint points was addressed to get optimum 2D key joint points of human body; And then pose estimation recovered by optimizing between the 2D projection of 3D human key joint points and 2D detection key joint points using QICA, which recovered the movement of human body perfectly, because this algorithm could acquire not only the global optimal solution, but the local optimal solution.


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