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

Chen, Shuang and Atapour-Abarghouei, Amir and Kerby, Jane and Ho, Edmond S. L. and Sainsbury, David C. G. and Butterworth, Sophie and Shum, Hubert P. H. (2022) 'A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip.', 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Ioannina, Greece, 27-30 Sept 2022.

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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://ieeexplore.ieee.org/xpl/conhome/1000364/all-proceedings
Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:19 July 2022
Date deposited:01 August 2022
Date of first online publication:2022
Date first made open access:01 October 2022

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