Quantitative Neuroanatomical Measurement on Photogrammetric Model: Validation Study.

Abstract

Objective: To examine and compare the accuracy of measurements obtained from photogrammetric models versus 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 using 2 different softwares to create three-dimensional models. Subsequently, 41 anatomical measurements (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, intrarater and interrater reliability, and intermodality agreement.

Results: Four photogrammetric models were created. Analysis revealed similar errors when comparing photogrammetric and direct measurements. Measurements from digital models exhibited robust reliability among repeated measures and different observers, supported by very high intraclass correlation coefficient 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 with direct measurement, displaying good agreement for smaller measurements and a systematic proportional bias with increasing measurement size.

Conclusions: Two photogrammetric models were validated as quantitative analysis techniques for laboratory neuroanatomical studies, showing acceptable measurement error, high intrarater and interrater reliability, and good to very good agreement compared with direct measurement on dry skulls, replacing expensive and time-consuming methods such as computed tomography scans and neuronavigation systems.