Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

Farhad Ghazvinian Zanjani; David Anssari Moin; Frank  Claessen; Teo  Cherici; Sarah  Parinussa; Arash Pourtaherian; Sveta Zinger; Peter H.N. de With

doi:10.1007/978-3-030-32254-0_15

Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

Farhad Ghazvinian Zanjani, David Anssari Moin, Frank Claessen, Teo Cherici, Sarah Parinussa, Arash Pourtaherian, Sveta Zinger, Peter H.N. de With

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

23 Citaten (Scopus)

Samenvatting

Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.

Originele taal-2	Engels
Titel	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Subtitel	22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V
Redacteuren	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Plaats van productie	Cham
Uitgeverij	Springer
Pagina's	128-136
Aantal pagina's	9
ISBN van elektronische versie	978-3-030-32254-0
ISBN van geprinte versie	978-3-030-32253-3
DOI's	https://doi.org/10.1007/978-3-030-32254-0_15
Status	Gepubliceerd - 2019
Evenement	22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019) - Shenzhen, China Duur: 13 okt. 2019 → 17 okt. 2019 https://www.miccai2019.org/

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11768 LNCS
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Congres

Congres	22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019)
Verkorte titel	MICCAI 2019
Land/Regio	China
Stad	Shenzhen
Periode	13/10/19 → 17/10/19
Internet adres	https://www.miccai2019.org/

Toegang tot document

10.1007/978-3-030-32254-0_15

Andere bestanden en links

Link to publication in Scopus

Citeer dit

Ghazvinian Zanjani, F., Anssari Moin, D., Claessen, F., Cherici, T., Parinussa, S., Pourtaherian, A., Zinger, S., & de With, P. H. N. (2019). Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (editors), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V (blz. 128-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11768 LNCS). Springer. https://doi.org/10.1007/978-3-030-32254-0_15

Ghazvinian Zanjani, Farhad ; Anssari Moin, David ; Claessen, Frank et al. / Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. redacteur / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Cham : Springer, 2019. blz. 128-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{00952ee0665e42c5978b113e8bac4151,

title = "Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans",

abstract = "Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.",

keywords = "3D point cloud, Deep learning, Instance segmentation, Intra-oral scan",

author = "{Ghazvinian Zanjani}, Farhad and {Anssari Moin}, David and Frank Claessen and Teo Cherici and Sarah Parinussa and Arash Pourtaherian and Sveta Zinger and {de With}, {Peter H.N.}",

year = "2019",

doi = "10.1007/978-3-030-32254-0_15",

language = "English",

isbn = "978-3-030-32253-3",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "128--136",

editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",

address = "Germany",

note = "22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019), MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

url = "https://www.miccai2019.org/",

}

Ghazvinian Zanjani, F, Anssari Moin, D, Claessen, F, Cherici, T, Parinussa, S, Pourtaherian, A, Zinger, S & de With, PHN 2019, Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (uitgave), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11768 LNCS, Springer, Cham, blz. 128-136, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019), Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32254-0_15

Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. / Ghazvinian Zanjani, Farhad; Anssari Moin, David; Claessen, Frank et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. uitgave / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Cham: Springer, 2019. blz. 128-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11768 LNCS).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

AU - Ghazvinian Zanjani, Farhad

AU - Anssari Moin, David

AU - Claessen, Frank

AU - Cherici, Teo

AU - Parinussa, Sarah

AU - Pourtaherian, Arash

AU - Zinger, Sveta

AU - de With, Peter H.N.

PY - 2019

Y1 - 2019

N2 - Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.

AB - Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score.

KW - 3D point cloud

KW - Deep learning

KW - Instance segmentation

KW - Intra-oral scan

UR - http://www.scopus.com/inward/record.url?scp=85075689427&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32254-0_15

DO - 10.1007/978-3-030-32254-0_15

M3 - Conference contribution

SN - 978-3-030-32253-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 128

EP - 136

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer

CY - Cham

T2 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019)

Y2 - 13 October 2019 through 17 October 2019

ER -

Ghazvinian Zanjani F, Anssari Moin D, Claessen F, Cherici T, Parinussa S, Pourtaherian A et al. Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, redacteurs, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. Cham: Springer. 2019. blz. 128-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32254-0_15

Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

Samenvatting

Publicatie series

Congres

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit