3D Deep Learning for Virtual Orbital Defect Reconstruction: A Precise and Automated Approach.

Abstract

Accurate virtual orbital reconstruction is crucial for preoperative planning. Traditional methods, such as the mirroring technique, are unsuitable for orbital defects involving both sides of the midline and are time-consuming and labor-intensive. This study introduces a modified 3D U-Net+++ architecture for orbital defects reconstruction, aiming to enhance precision and automation. The model was trained and tested with 300 synthetic defects from cranial spiral CT scans. The method was validated in 15 clinical cases of orbital fractures and evaluated using quantitative metrics, visual assessments, and a 5-point Likert scale, by 3 surgeons. For synthetic defect reconstruction, the network achieved a 95% Hausdorff distance (HD95) of<2.0 mm, an average symmetric surface distance (ASSD) of ∼0.02 mm, a surface Dice similarity coefficient (Surface DSC)>0.94, a peak signal-to-noise ratio (PSNR)>35 dB, and a structural similarity index (SSIM)>0.98, outperforming the compared state-of-the-art networks. For clinical cases, the average 5-point Likert scale scores for structural integrity, edge consistency, and overall morphology were>4, with no significant difference between unilateral and bilateral/trans-midline defects. For clinical unilateral defect reconstruction, the HD95 was ∼2.5 mm, ASSD<0.02 mm, Surface DSC>0.91, PSNR>30 dB, and SSIM>0.99. The automatic reconstruction process took ∼10 seconds per case. In conclusion, this method offers a precise and highly automated solution for orbital defect reconstruction, particularly for bilateral and trans-midline defects. We anticipate that this method will significantly assist future clinical practice.