Evaluating a Photogrammetry-Based Video for Undergraduate Anatomy Education.

Modern anatomy education has benefitted from the development of a wide range of digital 3D resources in the past decades, but the impact of the COVID-19 pandemic has sparked an additional demand for high-quality online learning resources. Photogrammetry provides a low-cost technique for departments to create their own photo-realistic 3D models of cadaveric specimens. However, to ensure accessibility, the design of the resulting learning resources should be carefully considered. We aimed to address this by creating a video based on a photogrammetry model of a cadaveric human lung. Students evaluated three different versions of this video in a Likert-type online survey. Most responding students found this type of video useful for their learning and helpful for the identification of anatomical structures in real cadaveric specimens. Respondents also showed a preference for specific design features such as a short video length, white text on black background, and the presence of captions. The positive student feedback is promising for the future development of photogrammetry-based videos for anatomy education and this study has provided pilot data to improve the accessibility of such videos.

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks.

PURPOSE: Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient's body. Maximising accuracy of the virtual models' inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. METHODS: HoloLens® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor's surface in a predefined position. The headset's camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset's lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset's camera. RESULTS: Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. CONCLUSION: Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.

Image Overlay Surgery Based on Augmented Reality: A Systematic Review.

Augmented Reality (AR) applied to surgical guidance is gaining relevance in clinical practice. AR-based image overlay surgery (i.e. the accurate overlay of patient-specific virtual images onto the body surface) helps surgeons to transfer image data produced during the planning of the surgery (e.g. the correct resection margins of tissue flaps) to the operating room, thus increasing accuracy and reducing surgery times. We systematically reviewed 76 studies published between 2004 and August 2018 to explore which existing tracking and registration methods and technologies allow healthcare professionals and researchers to develop and implement these systems in-house. Most studies used non-invasive markers to automatically track a patient's position, as well as customised algorithms, tracking libraries or software development kits (SDKs) to compute the registration between patient-specific 3D models and the patient's body surface. Few studies combined the use of holographic headsets, SDKs and user-friendly game engines, and described portable and wearable systems that combine tracking, registration, hands-free navigation and direct visibility of the surgical site. Most accuracy tests included a low number of subjects and/or measurements and did not normally explore how these systems affect surgery times and success rates. We highlight the need for more procedure-specific experiments with a sufficient number of subjects and measurements and including data about surgical outcomes and patients' recovery. Validation of systems combining the use of holographic headsets, SDKs and game engines is especially interesting as this approach facilitates an easy development of mobile AR applications and thus the implementation of AR-based image overlay surgery in clinical practice.

Application of Photogrammetry in Biomedical Science.

Photogrammetry is an upcoming technology in biomedical science as it provides a non-invasive and cost-effective alternative to established 3D imaging techniques such as computed tomography. This review introduces the photogrammetry approaches currently used for digital 3D reconstruction in biomedical science and discusses their suitability for different applications. It aims to offer the reader a better understanding of photogrammetry as a 3D reconstruction technique and to provide some guidance on how to choose the appropriate photogrammetry approach for their research area (including single- versus multi-camera setups, structure-from-motion versus conventional photogrammetry and macro- versus microphotogrammetry) as well as guidance on how to obtain high-quality data. This review highlights some key advantages of photogrammetry for a variety of applications in biomedical science, but it also discusses the limitations of this technique and the importance of taking steps to obtain high-quality images for accurate 3D reconstruction.