Stochastic Anomaly Simulation for Maxilla Completion from Cone-Beam Computed Tomography

Automated alveolar cleft defect restoration from cone beam computed tomography (CBCT) remains a challenging task, considering large morphological variations due to inter-subject abnormal maxilla development processes and a small cohort of clinical data. Existing works relied on rigid or deformable registration to borrow bony tissues from an unaffected side or a template for bony tissue filling. However, they lack harmony with the surrounding irregular maxilla structures and are limited when faced with bilateral defects. In this paper, we present a stochastic anomaly simulation algorithm for defected CBCT generation, combating limited clinical data and burdensome volumetric image annotation. By respecting the facial fusion process, the proposed anomaly simulation algorithm enables plausible data generation and relieves gaps from clinical data. We propose a weakly supervised volumetric inpainting framework for cleft defect restoration and maxilla completion, taking advantage of anomaly simulation-based data generation and the recent success of deep image inpainting techniques. Extensive experimental results demonstrate that our approach effectively restores defected CBCTs with performance gains over state-of-the-art methods.