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Currently, 3D image technology is being used for animation production. As for the current Traditional animation production technology, most of them still stay in two-dimensional animation production. Although two-dimensional animation is the main form of expression in the current animation field, due to the limitations of its production methods, the emotional contrast and special effects of animated characters in animation are not ideal and cannot be built on strong audience resonance. In order to solve these problems, this paper proposes a design of 3D image technology animation character modeling system under the background of Metaverse. The system mainly consists of a virtual character construction section and a special effects production section. After testing the character modeling rate, model detail detection rate, special effects production ability, and system error rate, the actual feasibility of the system was finally determined. After the completion of the system design, the system performance is compared with the data produced by Traditional animation. The results show that compared with traditional animation, the degree of detail description of this animation has reached 70%, and the ability to describe head and body details has reached 80%. Its average special effect production efficiency is 3 h. Except for the time efficiency of special effect production, other data are better than the Traditional animation production methods. The excellent emotional performance and special effects production of animation have also led to a 90% satisfaction survey result for the 3D imaging technology animation character modeling system. Finally, according to the data, the conclusion is drawn that the 3D image technology animation character modeling system outperforms the traditional animation production methods in the aspects of animation emotion contrast and special effect production, which improves the problems in these two aspects of animation production to a certain extent.

Background: This study explored how Taiwanese schoolchildren perceive the appearance of their peers with and without cleft lip and palate (CLP) and whether this perception affects social interactions. We specifically focused on early adolescents with surgically repaired CLP to assess the impact of residual craniofacial deformities. Methods: A cross-sectional design was used, analyzing three-dimensional (3D) surface images of twenty patients with repaired CLP and five without. A total of 91 schoolchildren (40 with CLP, 51 without) served as raters. Participants used a Likert scale to rate images on facial appearance and perceived social acceptance. The study also measured the reliability of its questionnaires using Cronbach’s alpha. Results: All participants successfully differentiated between images of children with and without CLP, though non-cleft participants had significantly better distinguishing abilities. Non-cleft raters consistently gave more positive appearance ratings to non-cleft images, a pattern less evident among cleft raters. While differences in awareness and acceptance between the two groups were not statistically significant, over half of all responses regarding social interaction were neutral. The questionnaires demonstrated high reliability, with Cronbach’s alpha values greater than 0.85. Conclusions: Despite the ability to perceive residual craniofacial differences, appearance alone did not significantly affect social interactions for early adolescent children with surgically repaired CLP in Taiwan. This suggests that other factors may play a larger role in social dynamics within this population.

Balloons made by cut fabric pieces are widely used in space research. To predict the blasting pressure of a balloon, we propose a novel method based on the non-contact test strain at a low internal pressure. The three-dimensional digital image correlation technique is introduced to measure the surface strain of the balloon. Representative regions of the balloon are selected as the test regions. A correction factor is proposed that accounts for the relationship between the internal pressure and the surface strain for the actual and the ideal balloon. By combining the maximum surface strain at a given internal pressure and the correction factor, we can predict the blasting pressure of the balloon. A blasting test is carried out to verify the feasibility of the predictive method. When the value of the ratio of the maximum test strain to the limiting strain reaches about a reference value, the absolute value of the deviation percentage between the predicted blasting pressure and the actual blasting pressure is less than 10%. The blasting pressure for balloon can be predicted accurately. This method does not require the balloon to be inflated to a high internal pressure, which improves the practicality of the prediction.

Organoids replicate tissue architecture and function and offer a unique opportunity to explore the impact of external perturbations in vitro. However, conducting large-scale screening procedures to investigate the effects of various stresses on cellular morphology and topology in these systems poses important challenges, including limitations in high-resolution three-dimensional (3D) imaging and accessible 3D analysis platforms. In this study, we introduce an AI-based multilevel segmentation and cellular topology pipeline for screening morphology and topology modifications in 3D cell culture at both the nuclear and cytoplasmic levels, as well as at the whole-organoid scale. We demonstrate the versatility of our approach through proof-of-concept experiments, encompassing well-characterized conditions and poorly explored mechanical stressors such as microgravity. By offering a user-friendly interface named 3DCellScope and a comprehensive set of tools for discovery-like assays in screening 3D organoid models, our pipeline demonstrates wide-ranging potential for applications in biomedical research. A universal AI-powered pipeline for fast, precise and plug-and-play image analysis of diverse 3D cellular structures across multiple imaging resolutions.

To master the fundamentals of image registration, there is no more comprehensive source than 2-D and 3-D Image Registration. In addition to delving into the relevant theories of image registration, the author presents their underlying algorithms. You'll also discover cutting-edge techniques to use in remote sensing, industrial, and medical applications. Examples of image registration are presented throughout, and the companion Web site contains all the images used in the book and provides links to software and algorithms discussed in the text, allowing you to reproduce the results in the text and develop images for your own research needs. 2-D and 3-D Image Registration serves as an excellent textbook for classes in image registration as well as an invaluable working resource.

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.

In vitro 3D organoid systems have revolutionized the modeling of organ development and diseases in a dish. Fluorescence microscopy has contributed to the characterization of the cellular composition of organoids and demonstrated organoids’ phenotypic resemblance to their original tissues. Here, we provide a detailed protocol for performing high-resolution 3D imaging of entire organoids harboring fluorescence reporters and upon immunolabeling. This method is applicable to a wide range of organoids of differing origins and of various sizes and shapes. We have successfully used it on human airway, colon, kidney, liver and breast tumor organoids, as well as on mouse mammary gland organoids. It includes a simple clearing method utilizing a homemade fructose–glycerol clearing agent that captures 3D organoids in full and enables marker quantification on a cell-by-cell basis. Sample preparation has been optimized for 3D imaging by confocal, super-resolution confocal, multiphoton and light-sheet microscopy. From organoid harvest to image analysis, the protocol takes 3 d. This protocol for clearing and high-resolution 3D imaging of entire organoids expressing fluorescence reporters or following immunolabeling enables confocal, super-resolution confocal, multiphoton and light-sheet microscopy to be performed.

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.Tissue-clearing methods are now allowing 3D imaging of intact tissues and some entire mammals. In this Review, Ueda and colleagues discuss the various tissue-clearing methods, related techniques and data analysis and management, as well as the application of these methods in neuroscience.

Presented is an overview of the image-analysis software platform Fiji, a distribution of ImageJ that updates the underlying ImageJ architecture and adds modern software design elements to expand the capabilities of the platform and facilitate collaboration between biologists and computer scientists. Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Sca l eS is a tissue clearing method for light and electron microscopy featuring stable tissue preservation for immunochemical and genetic labeling of tissue for 3D signal rendering. The technique enables quantitative and reproducible reconstructions of aged and diseased tissue in animal models and patients for high resolution optical pathology. Optical clearing methods facilitate deep biological imaging by mitigating light scattering in situ . Multi-scale high-resolution imaging requires preservation of tissue integrity for accurate signal reconstruction. However, existing clearing reagents contain chemical components that could compromise tissue structure, preventing reproducible anatomical and fluorescence signal stability. We developed Sca l eS, a sorbitol-based optical clearing method that provides stable tissue preservation for immunochemical labeling and three-dimensional (3D) signal rendering. Sca l eS permitted optical reconstructions of aged and diseased brain in Alzheimer's disease models, including mapping of 3D networks of amyloid plaques, neurons and microglia, and multi-scale tracking of single plaques by successive fluorescence and electron microscopy. Human clinical samples from Alzheimer's disease patients analyzed via reversible optical re-sectioning illuminated plaque pathogenesis in the z axis. Comparative benchmarking of contemporary clearing agents showed superior signal and structure preservation by Sca l eS. These findings suggest that Sca l eS is a simple and reproducible method for accurate visualization of biological tissue.