Publications
2022
M, Leclercq; A, Ruellas; M, Gurgel; M, Yatabe; J, Bianchi; L, Cevidanes; et al,
Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces. Journal Article
In: Semantic Scholar, 2022.
Abstract | Links | BibTeX | Tags: bone segmentation, Deep Learning, dental crown, intra-oral surface, Universal label id
@article{Bianchi2023i,
title = {Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces.},
author = {Leclercq M and Ruellas A and Gurgel M and Yatabe M and Bianchi J and Cevidanes L and et al},
url = {https://www.semanticscholar.org/paper/FiboSeg%3A-Fully-automated-segmentation-of-upper-and-Leclercq-Ruellas/7269780afbd9c060e4509465f2b24d7fdbc35924},
year = {2022},
date = {2022-01-01},
journal = {Semantic Scholar},
abstract = {In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97.},
keywords = {bone segmentation, Deep Learning, dental crown, intra-oral surface, Universal label id},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97.
M, Leclercq; A, Ruellas; M, Gurgel; M, Yatabe; J, Bianchi; L, Cevidanes; et al,
Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces. Journal Article
In: Semantic Scholar, 2022.
@article{Bianchi2023i,
title = {Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces.},
author = {Leclercq M and Ruellas A and Gurgel M and Yatabe M and Bianchi J and Cevidanes L and et al},
url = {https://www.semanticscholar.org/paper/FiboSeg%3A-Fully-automated-segmentation-of-upper-and-Leclercq-Ruellas/7269780afbd9c060e4509465f2b24d7fdbc35924},
year = {2022},
date = {2022-01-01},
journal = {Semantic Scholar},
abstract = {In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97.
2022 |
M, Leclercq; A, Ruellas; M, Gurgel; M, Yatabe; J, Bianchi; L, Cevidanes; et al,: Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces.. In: Semantic Scholar, 2022. (Type: Journal Article | Abstract | Links | BibTeX | Tags: bone segmentation, Deep Learning, dental crown, intra-oral surface, Universal label id)@article{Bianchi2023i,In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97. |