# A Reproducible 3D Classification of Orbital Morphology Derived from CBCT and FBCT Segmentation

## TL;DR

This paper introduces a reproducible 3D classification system for orbital morphology using CT scans, which could aid in surgical planning when anatomical references are unavailable.

## Contribution

A novel 3D morphological classification of orbits using clustering and statistical validation for surgical applications.

## Key findings

- Three distinct orbital morphotypes were identified using k-means clustering and validated with high accuracy.
- Male orbits were significantly deeper and wider than female orbits.
- A simplified decision algorithm achieved 82.1% classification accuracy.

## Abstract

Background: Accurate reconstruction of the orbit after trauma or oncological resection requires reliable anatomical references. In unilateral cases, the contralateral orbit can guide repair, but bilateral injuries or pathologies remove this option. To address this problem, we developed a new morphological classification of orbits based on three linear dimensions. Methods: A total of 499 orbits from patients of Caucasian descent (age 8–88 years) were analyzed using three-dimensional models generated from cone-beam and fan-beam CT scans. Orbital depth (D), height (H), and width (W) were measured, and proportional indices were calculated. K-means clustering (k = 3) identified recurring morphotypes, validated by linear discriminant analysis (LDA) and supported by ANOVA, Kruskal–Wallis, and correlation tests (age and sex). Results: Three morphotypes were identified: Tall & Broad (type A, 33.5%), Deep & Broad (type B, 30.2%), and Compact (type C, 36.2%). All dimensions differed significantly between groups (ANOVA, p < 1 × 10−16; η2 = 0.40–0.51). Male orbits were significantly deeper and wider than female ones (p < 0.001). LDA demonstrated excellent separation with 97.5% accuracy. A simplified decision algorithm achieved 82.1% classification accuracy. In situations where only orbital depth could be measured, an alternative cut-off-based method reached 61.5% accuracy, with type B and C better distinguished than type A. Conclusions: The proposed classification provides a reproducible framework for describing orbital morphology. It may serve as a reference in cases where local anatomy is disrupted or the contralateral orbit is unavailable. Even millimeter-scale differences in orbital dimensions may correspond to clinically relevant changes in orbital volume and globe position, underlining the potential usefulness of this system in surgical planning.