Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Holger, Willems"'
Autor:
Nermin Morgan, Adriaan Van Gerven, Andreas Smolders, Karla de Faria Vasconcelos, Holger Willems, Reinhilde Jacobs
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT)
Externí odkaz:
https://doaj.org/article/99d2d32e203d4892b4e92122f40555ad
Autor:
Rocharles Cavalcante Fontenele, Maurício do Nascimento Gerhardt, Fernando Fortes Picoli, Adriaan Van Gerven, Stefanos Nomidis, Holger Willems, Deborah Queiroz Freitas, Reinhilde Jacobs
Publikováno v:
Clinical Oral Implants Research.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bae9501d9dc5760b4d8f295991bb5199
https://doi.org/10.1111/clr.14063
https://doi.org/10.1111/clr.14063
Autor:
Khalid Ayidh Alqahtani, Reinhilde Jacobs, Andreas Smolders, Adriaan Van Gerven, Holger Willems, Sohaib Shujaat, Eman Shaheen
Publikováno v:
European journal of orthodontics.
Summary Objective Tooth segmentation and classification from cone-beam computed tomography (CBCT) is a prerequisite for diagnosis and treatment planning in the majority of digital dental workflows. However, an accurate and efficient segmentation of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::066eb57159032cd9221b4472c9fab77c
https://pubmed.ncbi.nlm.nih.gov/36099419
https://pubmed.ncbi.nlm.nih.gov/36099419
Autor:
Rocharles Cavalcante Fontenele, Maurício do Nascimento Gerhardt, Jáder Camilo Pinto, Adriaan Van Gerven, Holger Willems, Reinhilde Jacobs, Deborah Queiroz Freitas
Publikováno v:
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Made available in DSpace on 2022-04-28T19:51:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Objectives: To assess the influence of dental fillings on the perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14b2476173166fad74cdf011e0edea10
Autor:
Adriaan Van Gerven, André Ferreira Leite, Hugo Gaêta-Araujo, Holger Willems, Reinhilde Jacobs, Myrthel Vranckx, Thomas Beznik, Pierre Lahoud
Publikováno v:
Clinical Oral Investigations. 25:2257-2267
To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68682e1355bf3bd1a1747a1160599341
https://doi.org/10.1007/s00784-020-03544-6
https://doi.org/10.1007/s00784-020-03544-6
Autor:
Flavia Preda, Nermin Morgan, Adriaan Van Gerven, Fernanda Nogueira-Reis, Andreas Smolders, Xiaotong Wang, Stefanos Nomidis, Eman Shaheen, Holger Willems, Reinhilde Jacobs
Publikováno v:
Journal of dentistry. 124
The present study investigated the accuracy, consistency, and time-efficiency of a novel deep convolutional neural network (CNN) based model for the automated maxillofacial bone segmentation from cone beam computed tomography (CBCT) images.A dataset
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a77ef1896587105804a095329c4ba6de
https://pubmed.ncbi.nlm.nih.gov/35872223
https://pubmed.ncbi.nlm.nih.gov/35872223
Autor:
Maurício do Nascimento Gerhardt, Rocharles Cavalcante Fontenele, André Ferreira Leite, Pierre Lahoud, Adriaan Van Gerven, Holger Willems, Andreas Smolders, Thomas Beznik, Reinhilde Jacobs
Publikováno v:
Journal of dentistry. 122
To assess the accuracy of a novel Artificial Intelligence (AI)-driven tool for automated detection of teeth and small edentulous regions on Cone-Beam Computed Tomography (CBCT) images.After AI training and testing with 175 CBCT scans (130 for trainin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85ab3019c077b299a5a05b6eea732c99
https://pubmed.ncbi.nlm.nih.gov/35461974
https://pubmed.ncbi.nlm.nih.gov/35461974
Autor:
Nermin Morgan, Adriaan Van Gerven, Andreas Smolders, Karla de Faria Vasconcelos, Holger Willems, Reinhilde Jacobs
Publikováno v:
Scientific reports. 12(1)
An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00fc90d07bcfd4df9205f5e31aa591c4
https://pubmed.ncbi.nlm.nih.gov/35525857
https://pubmed.ncbi.nlm.nih.gov/35525857
Autor:
Holger Willems, Adriaan Van Gerven, Andreas Smolders, Reinhilde Jacobs, Eman Shaheen, André Ferreira Leite, Khalid Alqahtani
Publikováno v:
Journal of dentistry. 115
Objectives Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning appro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40bc726998d1a1679ef5dbbfba725a26
https://pubmed.ncbi.nlm.nih.gov/34710545
https://pubmed.ncbi.nlm.nih.gov/34710545
Autor:
Holger Willems, Mostafa EzEldeen, Thomas Beznik, André Ferreira Leite, Adriaan Van Gerven, Reinhilde Jacobs, Pierre Lahoud
INTRODUCTION: Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc436897532816203c9a75af9d10d05a
https://lirias.kuleuven.be/handle/123456789/673051
https://lirias.kuleuven.be/handle/123456789/673051