Three-dimensional deep learning to automatically generate cranial implant geometry.

ABSTRACT: We present a 3D deep learning framework that can generate a complete cranial model using a defective one. The Boolean subtraction between these two models generates the geometry of the implant required for surgical reconstruction. There is little or no need for post-processing to eliminate noise in the implant model generated by the proposed approach. The framework can be used to meet the repair needs of cranial imperfections caused by trauma, congenital defects, plastic surgery, or tumor resection. Traditional implant design methods for skull reconstruction rely on the mirror operation. However, these approaches have great limitations when the defect crosses the plane of symmetry or the patient's skull is asymmetrical. The proposed deep learning framework is based on an enhanced three-dim

SUBMITTER: Wu CT 

PROVIDER: S-EPMC8854612 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature