Development and Validation of a High-Fidelity 3D Modeling Pipeline for Cadaveric Head and Neck Specimens in Skull Base Neurosurgery

Objective: This study aimed to develop and validate a reproducible pipeline for creating high-fidelity 3D digital models of cadaveric head and neck specimens, focusing on skull base anatomy. We aim to enhance anatomical study and surgical planning in skull base neurosurgery by providing accurate, accessible, and quantifiable digital representations.

Methods: We defined a comprehensive setup including a photographic suite, turning table, enhanced light exposure, operative microscope, and endoscope. Human cadaveric specimens (n = 10) were prepared with surgical exposures relevant to skull base approaches. Our acquisition process incorporated a professional camera, an operative microscope, 0 and 30-degree endoscopes, and an exoscope. Still, frames and video were collected from all devices. An in-house Python script was developed to extract high-quality frames from video recordings. Data was processed using photogrammetry software to generate point clouds, meshes, 3D models, and textures ([Fig. 1]). Models were optimized for anatomical accuracy and computational efficiency. A reference space was established using reference point markers on specimens, validated against stereotactic neuronavigation data. The pipeline was used to capture and reconstruct anterior, middle, and posterior cranial fossae structures and the pterygopalatine and infratemporal fossae. Quantitative measurements were performed on digital models and compared with neuronavigation findings to assess spatial accuracy and reliability. External open-source software was also tested for exploring point clouds and meshes; 3D measurements were repeated and compared here ([Fig. 2]).

Fig. 1

Fig. 2

Results: An average of 600 photographs were used to construct each model (1,500–2,000 frames were extracted from videos, with an adjustable range of extracted frames per second). The optimized pipeline consistently produced high-fidelity 3D models of cadaveric specimens. Key findings include the successful integration of multiple imaging modalities provided comprehensive anatomical details; the custom Python script for video processing significantly enhanced input data quality for photogrammetry and allowed customization of the workload; generated 3D models demonstrated excellent visual fidelity, closely representing original specimens in color, texture, and structural detail; quantitative measurements on digital models showed strong correlation with neuronavigation data, confirming spatial accuracy; high-definition volumetric models of the nasal corridor, paranasal fossae, and cranial fossae structures were achieved. Relevant points of interest were successfully labeled for educational use, while distances, areas, and volumes were collected for quantitative investigations.

Conclusion: This study demonstrates the feasibility and accuracy of using an optimized multi-modal imaging pipeline to create high-fidelity 3D digital models of intricate skull base anatomy. This approach opens new possibilities for skull base surgery education, research, and potential clinical applications. This pipeline represents a significant step forward in digital anatomical representation for skull base neurosurgery.

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Artikel online veröffentlicht:
07. Februar 2025