A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip

TL;DR

This study explores using deep learning-based image inpainting to generate non-cleft lip images from cleft lip images, aiming to assist surgical planning while ensuring patient privacy.

Contribution

It introduces a novel deep learning method for inpainting cleft lips to produce realistic non-cleft images, aiding surgical visualization.

Findings

Method successfully covers cleft lip regions in images

Generated images are validated by expert surgeons

Feasibility demonstrated on real-world datasets

Abstract

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in improving surgical outcomes. If AI can be used to predict what a repaired cleft lip would look like, surgeons could use it as an adjunct to adjust their surgical technique and improve results. To explore the feasibility of this idea while protecting patient privacy, we propose a deep learning-based image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a cleft. Our experiments are conducted on two real-world cleft lip datasets and are assessed by expert cleft lip surgeons to demonstrate the feasibility of the proposed method.