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Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview.
Zhang, Lang; Li, Wang; Lv, Jinxun; Xu, Jiajie; Zhou, Hengyu; Li, Gen; Ai, Keqi.
Afiliação
  • Zhang L; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Li W; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China. Electronic address: wang.l@cqut.edu.cn.
  • Lv J; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Xu J; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Zhou H; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Li G; School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Ai K; Department of Radiology, Xinqiao Hospital, Army Medical University, Chongqing 400037, China. Electronic address: hummer198625@tmmu.edu.cn.
J Dent ; 138: 104727, 2023 11.
Article em En | MEDLINE | ID: mdl-37769934
ABSTRACT

OBJECTIVES:

This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched).

RESULTS:

These segmentation methods can be mainly divided into two categories traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value.

CONCLUSION:

Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature. CLINICAL

SIGNIFICANCE:

Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Cirurgia Bucal / Dente Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Cirurgia Bucal / Dente Idioma: En Ano de publicação: 2023 Tipo de documento: Article