3D Visualization Processes for Recreating and Studying Organismal Form

A Novel Multidisciplinary Approach for Reptile Movement and Behavior Analysis

The study of animals' activity and behavior in the wild is an extremely challenging task. Although tri-axial accelerometers are invaluable for behavioral analyses, their use is more frequent in large charismatic endotherms with limited application in ectotherms. The scarce utilization of this methodology on small-size reptiles is focused on animals' activity and energetics, showing few records of rapid displays and behavior signals. Here, we present a novel multidisciplinary approach capable of advancing research on reptiles' behavior. Our proposed approach uses advanced technologies for the digitization, reconstruction and visualization of reptiles and their behavior. We (i) record movement through tri-axial accelerometers, video cameras, and motion capture systems; (ii) ground-truth data through the video records; (iii) develop realistically accurate 3D avatars of the recorded movement for visualization purposes, and (iv) archive data on a Behavior Pattern Database. As case studies, we used two small Mediterranean reptiles, the lizard Laudakia cypriaca and the snake Dolichophis jugularis. Through our approach, we successfully recorded, ground-truthed, and labeled for the first time, several detailed movements and behaviors of the two case study species. We developed an accurate digital overview of those movements using motion capture and 3D animal reconstruction. Finally, we structured a database for archiving all behavioral data and demonstrated how those archives can be used for advancing behavioral research, providing ecological insights into this animal group. Our approach can enhance research on reptiles' behavior by contributing to the analysis of complex or isolated behaviors, poorly studied, such as signals and social interactions, providing valuable insights and assisting behavioral analysis.

Exploring the potential and limitations of artificial intelligence in animal anatomy

BACKGROUND

Artificial intelligence (AI) is revolutionizing veterinary medicine, particularly in the domain of veterinary anatomy. At present, there is no existing review article in the literature that examines the prospects and challenges associated with the use of AI in animal anatomy education.

STUDY DESIGN

Narrative review.

OBJECTIVE

This review article explores the prospects and drawbacks of AI applications in veterinary anatomy. Anatomy and AI-powered diagnostic systems enhance clinical examination, diagnosis, and treatment by analyzing vast datasets, improving accuracy, and detecting subtle anomalies.

METHODS

We reviewed and analyzed recent literature on AI applications in veterinary anatomy education, emphasizing their potential, limitations, and future directions..

CONCLUSION

In veterinary anatomy education, AI integrates advanced tools like three-dimensional (3D) models, virtual reality (VR), and augmented reality (AR), offering dynamic and interactive learning experiences to students as well as the faculty of veterinary institutions across the globe. Despite these advantages, AI faces challenges such as the need for extensive, high-quality data, potential biases, and issues with algorithmic transparency. Additionally, virtual dissection and educational tools may impact hands-on learning and ethical and legal concerns regarding data privacy must be addressed. Balancing AI integration with traditional skills and addressing these challenges will maximize AI's benefits in veterinary anatomy and ensure comprehensive veterinary care.

Hyperspectral imaging in animal coloration research: A user-friendly pipeline for image generation, analysis, and integration with 3D modeling

Hyperspectral imaging-a technique that combines the high spectral resolution of spectrophotometry with the high spatial resolution of photography-holds great promise for the study of animal coloration. However, applications of hyperspectral imaging to questions about the ecology and evolution of animal color remain relatively rare. The approach can be expensive and unwieldy, and we lack user-friendly pipelines for capturing and analyzing hyperspectral data in the context of animal color. Fortunately, costs are decreasing and hyperspectral imagers are improving, particularly in their sensitivity to wavelengths (including ultraviolet) visible to diverse animal species. To highlight the potential of hyperspectral imaging for animal coloration studies, we developed a pipeline for capturing, sampling, and analyzing hyperspectral data (here, in the 325 nm to 700 nm range) using avian museum specimens. Specifically, we used the pipeline to characterize the plumage colors of the King bird-of-paradise (Cicinnurus regius), Magnificent bird-of-paradise (C. magnificus), and their putative hybrid, the King of Holland's bird-of-paradise (C. magnificus x C. regius). We also combined hyperspectral data with 3D digital models to supplement hyperspectral images of each specimen with 3D shape information. Using visual system-independent methods, we found that many plumage patches on the hybrid King of Holland's bird-of-paradise are-to varying degrees-intermediate relative to those of the parent species. This was true of both pigmentary and structurally colored plumage patches. Using visual system-dependent methods, we showed that only some of the differences in plumage patches among the hybrid and its parent species would be perceivable by birds. Hyperspectral imaging is poised to become the gold standard for many animal coloration applications: comprehensive reflectance data-across the entire surface of an animal specimen-can be obtained in a matter of minutes. Our pipeline provides a practical and flexible roadmap for incorporating hyperspectral imaging into future studies of animal color.

Applying 3D Models of Giant Salamanders to Explore Form-Function Relationships in Early Digit-Bearing Tetrapods

Extant salamanders are used as modern analogs of early digit-bearing tetrapods due to general similarities in morphology and ecology, but the study species have been primarily terrestrial and relatively smaller when the earliest digit-bearing tetrapods were aquatic and an order of magnitude larger. Thus, we created a 3D computational model of underwater walking in extant Japanese giant salamanders (Andrias japonicus) using 3D photogrammetry and open-access graphics software (Blender) to broaden the range of testable hypotheses about the incipient stages of terrestrial locomotion. Our 3D model and software protocol represent the initial stages of an open-access pipeline that could serve as a "one-stop-shop" for studying locomotor function, from creating 3D models to analyzing the mechanics of locomotor gaits. While other pipelines generally require multiple software programs to accomplish the different steps in creating and analyzing computational models of locomotion, our protocol is built entirely within Blender and fully customizable with its Python scripting so users can devote more time to creating and analyzing models instead of navigating the learning curves of several software programs. The main value of our approach is that key kinematic variables (e.g. speed, stride length, and elbow flexion) can be easily altered on the 3D model, allowing scientists to test hypotheses about locomotor function and conduct manipulative experiments (e.g. lengthening bones) that are difficult to perform in vivo. The accurate 3D meshes (and animations) generated through photogrammetry also provide exciting opportunities to expand the abundance and diversity of 3D digital animals available for researchers, educators, artists, conservation biologists, etc. to maximize societal impacts.

An in situ digital synthesis strategy for the discovery and description of ocean life

Revolutionary advancements in underwater imaging, robotics, and genomic sequencing have reshaped marine exploration. We present and demonstrate an interdisciplinary approach that uses emerging quantitative imaging technologies, an innovative robotic encapsulation system with in situ RNA preservation and next-generation genomic sequencing to gain comprehensive biological, biophysical, and genomic data from deep-sea organisms. The synthesis of these data provides rich morphological and genetic information for species description, surpassing traditional passive observation methods and preserved specimens, particularly for gelatinous zooplankton. Our approach enhances our ability to study delicate mid-water animals, improving research in the world's oceans.

replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

New Method for Rapid 3D Reconstruction of Semi-Transparent Underwater Animals and Structures

Morphological features are the primary identifying properties of most animals and key to many comparative physiological studies, yet current techniques for preservation and documentation of soft-bodied marine animals are limited in terms of quality and accessibility. Digital records can complement physical specimens, with a wide array of applications ranging from species description to kinematics modeling, but options are lacking for creating models of soft-bodied semi-transparent underwater animals. We developed a lab-based technique that can live-scan semi-transparent, submerged animals, and objects within seconds. To demonstrate the method, we generated full three-dimensional reconstructions (3DRs) of an object of known dimensions for verification, as well as two live marine animals-a siphonophore and an amphipod-allowing detailed measurements on each. Techniques like these pave the way for faster data capture, integrative and comparative quantitative approaches, and more accessible collections of fragile and rare biological samples.