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

Chen, Shuang; Atapour-Abarghouei, Amir; Kerby, Jane; Ho, Edmond S.L.; Sainsbury, David C.G.; Butterworth, Sophie; Shum, Hubert P.H.

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 learningbased image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a celft. 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.

Citation

Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022, September). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
• Start Date: Sep 27, 2022
• End Date: Sep 30, 2022
• Acceptance Date: Jul 19, 2022
• Online Publication Date: Nov 4, 2022
• Publication Date: 2022
• Deposit Date: Aug 1, 2022
• Publicly Available Date: Oct 1, 2022
• Series ISSN: 2641-3604,2641-3590
• DOI: https://doi.org/10.1109/bhi56158.2022.9926917
• Public URL: https://durham-repository.worktribe.com/output/1135801