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Evaluating the Precision of Automatic Segmentation of Teeth, Gingiva and Facial Landmarks for 2D Digital Smile Design Using Real-Time Instance Segmentation Network

Authors
 Seulgi Lee  ;  Jong-Eun Kim 
Citation
 JOURNAL OF CLINICAL MEDICINE, Vol.11(3) : 852, 2022-02 
Journal Title
JOURNAL OF CLINICAL MEDICINE
Issue Date
2022-02
Keywords
2D candid facial image ; YOLACT++ ; deep learning ; detection ; digital dentistry ; digital smile design ; segmentation
Abstract
Digital smile design (DSD) technology, which takes pictures of patients' faces together with anterior dentition and uses them for prosthesis design, has been recently introduced. However, the limitation of DSD is that it evaluates a patient with only one photograph taken in a still state, and the patient's profile cannot be observed from various viewpoints. Therefore, this study aims to segment the patient's anterior teeth, gingiva and facial landmarks using YOLACT++. We trained YOLACT++ on the annotated data of the teeth, lips and gingiva from the Flickr-Faces-HQ (FFHQ) data. We evaluated that the model trained by 2D candid facial images for the detection and segmentation of smile characteristics. The results show the possibility of an automated smile characteristic identification system for the automatic and accurate quantitative assessment of a patient's smile.
Files in This Item:
T202200529.pdf Download
DOI
10.3390/jcm11030852
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Prosthodontics (보철과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jong Eun(김종은) ORCID logo https://orcid.org/0000-0002-7834-2524
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188040
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