BioFace3D: An end-to-end open-source software for automated extraction of potential 3D facial biomarkers from MRIscans

Abstract

Background and Objectives: Facial dysmorphologies have emerged as potential critical indicators in the diagnosis and prognosis of genetic, psychotic, and rare disorders. While some conditions present with severe dysmorphologies, others exhibit subtler traits that may not be perceivable to the human eye, requiring the use of precise quantitative tools for accurate identification. Manual annotation remains time-consuming and prone to inter- and intra-observer variability. Existing tools provide partial solutions, but no end-to-end automated pipeline integrates the full process of 3D facial biomarker extraction from magnetic resonance imaging. Methods and Results: We introduce BioFace3D, an open-source pipeline designed to automate the discovery of potential 3D facial biomarkers from magnetic resonance imaging. BioFace3D consists of three automated modules: (i) 3D facial model extraction from magnetic resonance images, (ii) deep learning-based registration of homologous anatomical landmarks, and (iii) computation of geometric morphometric biomarkers from landmark coordinates. Conclusions: The evaluation of BioFace3D is performed both at a global level and within each individual module, through a series of exhaustive experiments using proprietary and public datasets, demonstrating the robustness and reliability of the results obtained by the tool. Source code, along with trained models, can be found at https://bitbucket.org/cv_her_lasalle