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Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.
da Silva, Renan Lucio Berbel; Yang, Su; Kim, DaEl; Kim, Jun Ho; Lim, Sang-Heon; Han, Jiyong; Kim, Jun-Min; Kim, Jo-Eun; Huh, Kyung-Hoe; Lee, Sam-Sun; Heo, Min-Suk; Yi, Won-Jin.
Afiliación
  • da Silva RLB; Discipline of Oral Radiology, Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil.
  • Yang S; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, South Korea.
  • Kim D; Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
  • Kim JH; Discipline of Oral Radiology, Department of Stomatology, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil.
  • Lim SH; Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
  • Han J; Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, 08826, South Korea.
  • Kim JM; Department of Electronics and Information Engineering, Hansung University, Seoul, 02876, South Korea.
  • Kim JE; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Huh KH; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Lee SS; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea.
  • Heo MS; Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, 03080, South Korea. hmslsh@snu.ac.kr.
  • Yi WJ; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, South Korea. wjyi@snu.ac.kr.
Sci Rep ; 14(1): 11750, 2024 05 23.
Article en En | MEDLINE | ID: mdl-38782964
ABSTRACT
Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada de Haz Cónico / Seno Frontal Límite: Adult / Female / Humans / Male Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada de Haz Cónico / Seno Frontal Límite: Adult / Female / Humans / Male Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido