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The premorbid geometry of the mandible is of significant relevance in jawreconstructive surgeries and occasionally unknown to the surgical team. In thispaper, an optimization framework is introduced to train deep models forcompletion (reconstruction) of the missing segments of the bone based on theremaining healthy structure. To leverage the contextual information of thesurroundings of the dissected region, the voxel-weighted Dice loss isintroduced. To address the non-deterministic nature of the shape completionproblem, we leverage a weighted multi-target probabilistic solution which is anextension to the conditional variational autoencoder (CVAE). This approachconsiders multiple targets as acceptable reconstructions, each weightedaccording to their conformity with the original shape. We quantify theperformance gain of the proposed method against similar algorithms, includingCVAE, where we report statistically significant improvements in bothdeterministic and probabilistic paradigms. The probabilistic model is alsoevaluated on its ability to generate anatomically relevant variations for themissing bone. As a unique aspect of this work, the model is tested on realsurgical cases where the clinical relevancy of its reconstructions and theircompliance with surgeon's virtual plan are demonstrated as necessary stepstowards clinical adoption.

Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery