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Deep learning segmentation of mandible with lower dentition from cone beam CT.
Kargilis, Daniel C; Xu, Winnie; Reddy, Samir; Ramesh, Shilpa Shree Kuduva; Wang, Steven; Le, Anh D; Rajapakse, Chamith S.
Afiliación
  • Kargilis DC; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA. dkargil1@jh.edu.
  • Xu W; Johns Hopkins University, Baltimore, USA. dkargil1@jh.edu.
  • Reddy S; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA.
  • Ramesh SSK; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA.
  • Wang S; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA.
  • Le AD; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA.
  • Rajapakse CS; University of Pennsylvania, 1 Founders Pavilion, 3400 Spruce Street, Philadelphia, PA, 19104-4283, USA.
Oral Radiol ; 2024 Aug 14.
Article en En | MEDLINE | ID: mdl-39141154
ABSTRACT

OBJECTIVES:

This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.

METHODS:

In an ambispective cross-sectional design, CBCT scans from two hospitals (2009-2019 and 2021-2022) constituted an internal dataset and external validation set, respectively. Manual segmentation informed CNN training, and evaluations employed Dice similarity coefficient (DSC) for volumetric accuracy. A blinded oral maxillofacial surgeon performed qualitative grading of CBCT scans and object meshes. Statistical analyses included independent t-tests and ANOVA tests to compare DSC across patient subgroups of gender, race, body mass index (BMI), test dataset used, age, and degree of metal artifact. Tests were powered for a minimum detectable difference in DSC of 0.025, with alpha of 0.05 and power level of 0.8.

RESULTS:

648 CBCT scans from 490 patients were included in the study. The CNN achieved high accuracy (average DSC 0.945 internal, 0.940 external). No DSC differences were observed between test set used, gender, BMI, and race. Significant differences in DSC were identified based on age group and the degree of metal artifact. The majority (80%) of object meshes produced by both manual and automatic segmentation were rated as acceptable or higher quality.

CONCLUSION:

We developed a model for automatic mandible and lower dentition segmentation from CBCT scans in a demographically diverse cohort including a high degree of metal artifacts. The model demonstrated good accuracy on internal and external test sets, with majority acceptable quality from a clinical grader.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Oral Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Japón
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Oral Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Japón