Digital models of equine bones via photogrammetry: low-cost innovation for interactive veterinary education

Modelos digitais de ossos equinos via fotogrametria: inovação de baixo custo para educação veterinária interativa

ABSTRACT:

Full-body dissection, the “gold standard” in anatomical education, can be hindered by events such as the COVID-19 pandemic. In the field of Veterinary Medicine, there are still significant challenges regarding the availability of accessible digital specimens to create a digital three-dimensional teaching atlas. To address these challenges, this study analyzed photogrammetry as a low-cost solution for digitizing natural equine bones, aiming to obtain interactive models of the digitized objects. Using equine specimens from the osteological collection of the Animal Science Museum at the Universidade Federal do Agreste de Pernambuco, the following study details the image capture and digitization of these bones using smartphones for data acquisition and the Kiri Engine application for the virtual reconstruction process. With subsequent manual editing and information addition, this study resulted in twenty seven 3D models of equine skeletal bones, and we demonstrated that photogrammetry can generate high-fidelity 3D models, with promising educational applications.

Key words:
osteology; horse; 3D models; technology; anatomy teaching

RESUMO:

A dissecção de corpo inteiro, o “padrão ouro” na educação anatômica, pode ser dificultada por eventos como a pandemia da COVID-19. No campo da Medicina Veterinária, ainda existem desafios significativos quanto à disponibilidade de espécimes digitais acessíveis para criar um atlas digital tridimensional de ensino. Para abordar esses desafios, este estudo analisou a fotogrametria como uma solução de baixo custo para digitalização de ossos naturais de equinos, visando obter modelos interativos dos objetos digitalizados. Utilizando espécimes equinos da coleção osteológica do Museu de Ciência Animal da Universidade Federal do Agreste de Pernambuco, o estudo a seguir detalha a captura de imagens e digitalização desses ossos usando smartphones para aquisição de dados e o aplicativo Kiri Engine para o processo de reconstrução virtual. Com subsequente edição manual e adição de informações, este estudo resultou em vinte e sete modelos 3D de ossos equinos e, demonstramos que a fotogrametria pode gerar modelos 3D de alta fidelidade, com aplicações educacionais promissoras.

Palavras-chave:
osteologia; cavalo; modelos 3D; tecnologia; ensino de anatomia

INTRODUCTION

Full-body dissection has been the “gold standard” in anatomical science education for centuries (LI et al., 2020). In the last decade, anatomical studies with cadavers have been complemented by various pedagogical tools, such as artistic diagrams, photographs, videos, and three-dimensional models (PETRICEKS et al., 2018; OLIVEIRA, 2023; PRASETYADI et al., 2023). Despite this technological advancement, veterinary anatomy still faces challenges in gathering high-quality and accessible digital material for the development of a teaching atlas with three-dimensional models, especially in domestic animals (DELORENZO et al., 2020; TSUBOI et al., 2020; KURT et al., 2023).

Through 3D digitization, physical objects are transformed into digital models using different methods. There are three approaches to 3D digitization based on the data acquisition method: (i) datasets acquired through computed tomography scans, magnetic resonance imaging, or ultrasound; (ii) sample data derived from scanning with a specific 3D scanner, such as laser or structured light scanners; and (iii) through data acquired via photographic records or video frames, as is the case with photogrammetry. The first two methods required equipment with high costs, including maintenance (KOTTNER et al., 2017; LI et al., 2020). LI et al. (2020) point out that, unlike photogrammetry, traditional scanning methods, such as Computed Tomography (CT) and scanners, required the need for expensive technologies and specialized professionals to operate.

Photogrammetry, which overlays two-dimensional (2D) photographs to create 3D digital models, enables the development of authentic and interactive digital models of cadaveric specimens (PETRICEKS et al., 2018; EROLIN et al., 2019; LI et al., 2020). This method; therefore, stands out as an excellent alternative to assist in the preservation, restoration, and research of digitized objects, including applications in anatomical studies (JANKIEWICZ et al., 2024).

This technique is described as highly accessible and user-friendly, utilizing mobile phone applications, basic medical-grade lamp setups, mobile phone cameras, and free computer software. This combination renders the technique easy, fast, and cost-effective, making it a potentially widely available alternative to conventional learning materials (JANKIEWICZ et al., 2024).

Cost reduction provided by photogrammetry in digitizing physical objects into three-dimensional figures proves to be an excellent tool to boost development in various fields of human knowledge. Furthermore, photogrammetry has proven to be an easy method to learn, without the need for in-depth knowledge to perform the scanning (NIGHTINGALE et al., 2021; KANTAROS et al., 2023). It is worth emphasizing the greater durability of 3D models, compared to preserved specimens; and therefore, the reduction in the use of toxic substances, such as formalin, which is an advantage for the health of students, technicians and teachers and for the preservation of the environment (DURRANI et al., 2025).

The technique of digitization through photogrammetry involves obtaining reliable information about the shape, dimensions, position, and orientation of objects and surfaces through photographs, without the need for physical contact to obtain the information. The pictures serve as the basis for generating data of three-dimensional and georeferenced representations of the space from which the images were obtained (SCHENK, 2005). The photogrammetry method involves taking measurements using specialized software, which identifies “key points” common among the records taken from various angles of the object to be digitized (EROLIN, 2019; BARACHO & TOLON, 2022).

There are few studies on 3D imaging in Animal Anatomy compared to the volume of research in Human Anatomy (KARABÖRK et al., 2009; CHRIST et al., 2018; LINTON et al., 2022; JIANG et al., 2024). Thus, this research presented the technique as a low-cost solution for digitizing physical objects, applying it to the digitization of natural equine bones; and subsequently, using the generated objects in the development of a three-dimensional anatomical atlas for studies in equine osteology.

MATERIALS AND METHODS

Specimens

Specimens from the equine osteological collection of the Animal Science Museum at the Laboratory of Animal Anatomy and Pathology (LAPA) of the Universidade Federal do Agreste de Pernambuco (UFAPE) were selected. The specimens-used in a disarticulated manner during practical classes for the Veterinary Medicine, Zootechnics, and Agronomy courses at UFAPE-were prepared using an osteological technique (maceration) 18 years prior to the image capture for photogrammetry and still preserved satisfactory morphological quality for studying the species’ particularities. Bones from the axial skeleton (skull, mandible, cervical, thoracic, lumbar, and sacral vertebrae, ribs, and sternum), thoracic appendicular skeleton (scapula, humerus, radius-ulna, carpus, metacarpals, and phalanges), and pelvic appendicular skeleton (pelvis, femur, patella, tibia, tarsus, metatarsals, and phalanges) were used. The bones were selected according to the most common morphological characteristics of each specimen.

Photogrammetry setup

The specimens were suspended for image capture using 0.40 mm nylon threads and a metal beam that was 2 meters high and 3 meters long. The recordings were made manually around the specimen, always in a counter clock wise motion, at a distance where the entire specimen was visible and in focus (ranging from 50 centimeters to 1 meter, depending on the size of the piece).

Artificial lighting from the room was used, and the windows were kept closed to prevent movement of the suspended material due to wind. Images were captured using a smartphone with the Kiri Engine application to produce three-dimensional images with the fidelity necessary for educational application.

The number of photos varied depending on the piece being digitized, capturing between 130 and 200 pictures for each specimen, taken at various heights (upper, middle, and lower, or even intermediate heights to these others depending on the length of the object). The specimen remained fixed and suspended while the operator slowly rotated the camera of the smartphone until completing a 360º rotation at the different heights of the anatomical structure.

The images were captured using a Samsung Galaxy A32 (Samsung GW3 64MP sensor S5KGW3, Manaus, Brasil) or a 2^nd generation iPhone SE (⅓” Apple iSight Camera 12 MP, Jundiaí, Brasil), depending on the sensitivity required for the digitization of the specimens. The Samsung was used for larger bones with less detail, while the iPhone was utilized for smaller and more detailed bones, such as the vertebrae.

Data processing

The Kiri Engine application¹ was used to generate the graphic objects, returning the 3D models in .OBJ file format along with texture files that define the colors of these models.

To remove noise and include labels for the specific characteristics of each bone, Blender 3.4 software² was used. The nomenclature used was based on the Nomina Anatomica Veterinaria (2017).

The model-viewer library³ was used to include interactive 3D objects on the LAPA website⁴. The technologies employed also included the React framework⁵ to the website development⁶, along with Git⁷ and GitHub⁸ for version control of the code.

RESULTS AND DISCUSSION

Simple materials and strategies were used, resulting in a low-cost study. The metal beam and nylon threads are simple, low-cost materials that allow all sides of the specimens to be freely accessible for viewing and capturing images from all angles.

In contrast, other researchers have utilized rotating platforms, professional cameras fixed at different heights, directed artificial lighting, and a controlled environment for image capture (PETRICEKS et al., 2018; KRAUSE et al., 2022; OLIVEIRA et al., 2023).

The use of a rotating platform or turntable for the object, along with a tripod and remote control, ensures the stability of the object, preventing blurry photos, achieving greater standardization of the images, and enhancing automation, thus reducing the time for image acquisition. This measure enhances standardization of the captures and allows the software to recognize as many coincident points as possible, thereby facilitating better 3D reconstruction of the object.

However, in the current study, the suspension of the object with transparent nylon was sufficient for stability, and adjustments were primarily needed for environmental and lighting conditions.

It was found that lighting, availability of space, and distance from other objects influence the quality of the model, potentially generating noise. Therefore, the activity was moved to a more spacious and better-lit room at LAPA. GRAHAM et al. (2017) report that, in photogrammetry, andnot even background and uncontrolled lighting conditions lead to long and tedious hours of pre-processing, complicating the representation of fine details. The authors caution that, due to the influence of environmental conditions, results may vary, making them irreproducible if the objective is to have two identical results. Figure 1 presents the results of the digitization of the same anatomical piece scanned in two distinct environments.

Figure 1
Comparison between equine femur models generated by photogrammetry in different environments using the Samsung Galaxy A32 smartphone. (A) Result of image capture in an environment with less controlled lighting and the presence of several objects that make the background of the images less uniform, failing to distinguish the object of interest from the rest of the environment; (B) image resulting from a more spacious environment with better lighting control, making the background of the photos more homogeneous and capturing fewer objects that could generate key points in the photos, isolating it from other objects.

For the purposes of this study, a good 3D reconstruction was characterized by its likeness to the original piece and the absence of noise in the final object after processing. The quality varies with the size and shape of the bone, as well as the sensitivity of the focus point of the capturing device. Correct focus is essential, as it minimizes systematic errors related to the position and distortion of the points captured in the image. Focus errors affect the projection of the image onto the camera plane, leading to inaccurate measurements. When the focus is not correctly adjusted, the position of the points may be displaced, introducing errors in the computerized reconstruction (DAI et al., 2014). PETRICEKS et al. (2018) emphasized that smooth and reflective surfaces often produce low-quality 3D images.

The camera of the Galaxy A32 smartphone produced images with lower definition compared to the 2^nd generation iPhone SE, resulting in reduced effectiveness in digitizing specimens with many details. Figure 2 presents the results obtained from the 5th thoracic vertebra using both devices. While the model generated by the Galaxy A32 exhibited several deformities, the model generated from the photos taken with the 2^nd generation iPhone SE displayed issues only with parts of the nylon thread, which are easily removable in a post-processing phase.

Figure 2
Comparison of models generated from the 5^th equine thoracic vertebra using the Galaxy A32 smartphone and the 2^nd generation iPhone SE.

It is noted that a higher number of megapixels in the camera is not necessarily associated with the final quality of the digitized object, but rather with the resolution and size of the generated file. The Apple sensor provides the device with a more sensitive focus, allowing close-up photos of small objects to have sufficient background blur so that the processing applied through photogrammetry cannot determine key points distant from the small target object. This helps prevent noise generated by the environment and provides better distinction of the object’s boundaries. This occurs because cameras equipped with advanced sensors use selective focus to isolate the target object from the background. As a result, the system can delineate the edges of the main object more precisely, facilitating the creation of a more accurate three-dimensional model (DAI et al., 2014).

In this research, it was observed that specimens with simpler shapes, such as the femur and humerus, required fewer photographs for good digitization. In contrast, there was greater difficulty in digitizing pieces with more complex shapes, such as the pelvis and sternum, requiring up to 200 photographs. LI et al. (2020) used an 8 MP camera from an iPhone SE and took 80 to 120 photographs for digitizing human cervical vertebrae, skulls, ribs, and femurs. The authors observed, as in this study, that the greater the structural complexity of the specimen, the more photos were required to reconstruct it in a good 3D model.

Nylon was chosen for suspending the specimens due to the difficulty that photogrammetry techniques face in finding key points on transparent objects, as noted by KARAMI et al. (2022). The transparency of this material minimized the likelihood of the threads appearing in the 3D reconstruction of the specimens. However, at times, the nylon thread was also reconstructed as part of the geometry of the digitized pieces, as seen in figure 3. In these cases, Blender software was utilized to remove these noises using the smoothing and/or removal tool by selecting portions of the file that exhibited some geometric deformities; the result of this process is exemplified in figure 3.

Figure 3
Tarsal bones before and after the removal of the nylon recreated as part of the specimen’s geometry.

Despite the good results obtained for most specimens, photogrammetry was unable to generate satisfactory models for equine ribs (Figure 4). Due to their elongated and very thin shape, the photos taken from the most dorsal and ventral viewpoints (considering the anatomical position of the bone) showed the portion closest to the focus well-defined, while the more distant portion was out of focus. LI et al. (2020) mention challenges in producing objects with curved surfaces by 3D printing and they concluded that curvature also has an impact on the photogrammetry process. However, there is a possibility that more recent techniques may be able to produce superior results with less effort (GAO et al., 2022).

Figure 4
Examples of attempts to digitize equine ribs using photogrammetry.

The 3D models were post-processed using the free and open-source 3D software Blender 3.4, in order to digitally remove nylon threads, eliminate artifacts from poorly reconstructed regions, and include labels, following the same methodology as KRAUSE et al. (2022). The labels were positioned after the entire digitization process, following the layout presented in illustrations and photographs of specimens in two-dimensional anatomical atlases (ASHDOWN & DONE, 2009; KÖNIG & LIEBICH, 2020). During the inclusion of subtitles, the movement of the object was checked to ensure that there was no excess information on the screen, avoiding confusing the user (Figure 5). Each finalized file was added to the digital platform integrated into the LAPA website, LAPA 3D - Equine Osteology, which is made available free of charge for students and professionals in Veterinary Medicine and related fields.

Figure 5
Presentation of the specimen in the LAPA (Laboratory of Animal Anatomyand Pathology) virtual atlas.

The Kiri Engine app proved effective in processing the images used for photogrammetry. GRAHAM et al. (2017) reported that photogrammetry is a cost-effective method to use when there is limited time for data capture. According to PETRICEKS et al. (2018); although, capturing images is relatively simple, the photogrammetric processing of the data can be quite complex. The authors note that processing hundreds of 2D images, applying surface mesh, and refining the final 3D image involves a lot of user interaction and therefore requires well-trained personnel. However, the application used in this work features an intuitive interface, with processing time that does not depend on the performance of the mobile device, making the process simple and relatively quick, as analyzed by PRASETYADI et al. (2023) and SANTOS et al. (2024). Thus, the existence of alternative methods and applications, such as Kiri Engine and Blender, allows for work of the same quality but with a much more accessible investment perspective. It even enables the use of photogrammetry in paleontological studies, with wild animals (BARBOSA, 2024), and even in crime scene reconstruction.

The development of platforms like these could have been of great assistance at the onset of the COVID-19 pandemic in 2020 for the study of veterinary anatomy. At that time, universities closed their doors due to health concerns, and the situation necessitated the predominant use of audiovisual resources and virtual questionnaires for educational purposes (CUCINOTTA & VANELLI, 2020; BRANDÃO et al., 2022). Free three-dimensional images from the digitization of real specimens could have served as a very useful alternative for teaching and learning anatomy courses during this period when social distancing was recommended.

MORICHON et al. (2024) stated that traditional teaching methods face challenges in effectively conveying three-dimensional concepts, especially in underserved areas. They demonstrated that 3D models obtained through photogrammetry techniques exhibit a complete mesh with a high level of detail and realistic texture (approximately 1 mm and 0.2 mm, respectively), with the potential to create viable and effective stereoscopic animations to enhance depth perception.

LI et al. (2020) highlighted that the most notable feature of the image production workflow through photogrammetry is that it operates without the need for scanners or computed tomography/magnetic resonance imaging datasets, thus allowing for a broader range of 3D printing technology for educational applications. Photogrammetry has significant potential in creating digital and interactive resources for anatomy education, enhancing the learning experience with photorealistic 3D models (MORICHON et al., 2024). Students and instructors can benefit from its authenticity, interactivity, ease of use, and digital nature (PETRICEKS et al., 2018). Some studies, such as those by LIMA et al. (2019) and LOZANO et al. (2018), have already described the use of scanning and 3D printing techniques for anatomical models in teaching and training; for example, the printing of osteological models for instructional activities and training in orthopedic procedures.

The integration of AR (Augmented Reality) and VR (Virtual Reality) has revolutionized veterinary anatomy education by enabling hybrid learning. These technologies can be made using photogrammetry and allow learners to visualize and perform dissections virtually, replacing actual animal dissections with digital ones, thus addressing ethical concerns. With fewer animals available for dissection and ethical issues, cadaveric dissections are supplemented or replaced by 3D visualizations. These images can create complete 3D virtual models of anatomical structures, facilitated by AI (Artificial Intelligence) and various software programs (JANKIEWICZ et al., 2024; JANKAPOOR et al., 2024).

The comparison between traditional and active methods highlights the need for a gradual transition, emphasizing the importance of adapting pedagogical approaches to the digital age. The acceptance of these innovations in anatomy education underscores the relevance of combining traditional methods, such as dissection, with innovative technologies to optimize the learning experience (LISBOA & DE ALMEIDA JÚNIOR, 2024).

These results are significant as they provide a practical alternative for education in veterinary anatomy, facilitating access to interactive teaching resources. However, it is acknowledged that the quality of digitization may vary depending on the geometric complexity of the specimens and the capture conditions.

CONCLUSION

Photogrammetry has proven to be an accessible technique for the digitization of natural bones, resulting in the generation of high-fidelity 3D models without the need for specific and expensive equipment. Future research should explore different environments and techniques to maximize the potential of digitizing anatomical specimens in various educational contexts, besides interdisciplinary studies with pedagogy and environmental impacts.

ACKNOWLEDGMENTS

This research was financially supported by Fundação de Amparo à Ciência e Tecnologia de Pernambuco (FACEPE), grant nº ARC-0002-5.05/23, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

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• CR-2024-0577.R2
• SOURCE OF ACQUISITION
  SOURCE OF ACQUISITION ¹<https://www.kiriengine.com/>. ²<https://www.blender.org/>. ³<https://modelviewer.dev/>. ⁴<http://www.lapa.ufape.edu.br/>. ⁵<https://react.dev/>. ⁶<https://modelos3d.vercel.app/>. ⁷<https://git-scm.com/>. ⁸<https://github.com/>.
• DATA AVAILABILITY STATEMENT
  The raw data that support the findings of this study are available from the corresponding author upon reasonable request.
• DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE
  The authors did not employ any artificial intelligence in this study.

Edited by

• ASSOCIATE EDITOR:
  Rudi Weiblen (0000-0002-1737-9817)

• SCIENTIFIC EDITOR:
  Roberta Blake (0000-0003-0037-5286)

Data availability

The raw data that support the findings of this study are available from the corresponding author upon reasonable request.

Publication Dates

• Publication in this collection
  16 Feb 2026
• Date of issue
  2026

History

• Received
  02 Nov 2024
• Accepted
  30 July 2025
• Reviewed
  04 Nov 2025