Quantitative Neuroanatomical measurement on Photogrammetric model: Validation study.

Abstract

Objective: To scrutinize and compare the accuracy of measurements obtained from photogrammetric models against direct measurements taken on dry skulls, with the aim to verify the feasibility of photogrammetry for quantitative analysis in microsurgical neuroanatomy.

Methods: Two dry human skulls were used. Each was scanned using the dual camera system of a smartphone The selected photos were separately processed two different software to create 3D models. Subsequently, 41 anatomical measures (both linear and curvilinear) based on common anatomical landmarks, were taken both directly on dry skulls and on photogrammetric models and compared. Analyzed factors included measurement error, intra- and inter-rater reliability and, the intermodality agreement.

Results: Four photogrammetric models were created. Our analysis revealed similar errors when comparing the photogrammetric and direct measurements. Measurements from digital models exhibited robust reliability among repeated measures and different observers, supported by very high Intraclass Correlation Coefficient (ICC) values. Mean measurement difference between Agisoft Metashape software-generated models and direct measurement was 0.01 cm with no systematic bias observed. Conversely, the Metascan app-derived models showed a mean measurement difference of -0.35 cm compared to direct measurement, displaying good agreement for smaller measurements and a systematic proportional bias with increasing measurement size.

Conclusions: The role of two photogrammetric models is validated as quantitative analysis technique for laboratory neuroanatomical studies showing acceptable measurement error, high intra- and inter-rater reliability as well as good to very good agreement compared to direct measurement on dry skulls, replacing the expensive and time-consuming methods like CT scans and neuronavigation system.