An improved pipeline for processing and analysis of facial surface images in medicine

This Mitacs proposal tackles several outstanding issues that must be addressed to complete development of a widely applicable pipeline for quantitative analysis of 3D facial shape in medicine. Here, we focus on specific applications of imaging pipelines in genetic syndrome diagnosis and facial surgery visualization and planning. The fellows will develop (a) cutting edge, deep learning models of 3D facial shape variation, (b) optimal strategies for defining boundaries between regions, (c) methods to infer high resolution skin color and texture from low resolution images, (d) and models that integrate 3D facial shape data with discrete and preference data for various purposes.

Faculty Supervisor:

Benedikt Hallgrimsson;Nils Daniel Forkert;Richard Hawkes

Student:

Jose David Aponte;Jordan Bannister

Partner:

AIndlit Medical Aesthetics

Discipline:

Biology

Sector:

Professional, scientific and technical services

University:

University of Calgary