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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
LanguageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V
EditorsDinggang Shen, Tianming Liu, Terry M. Peters
Place of PublicationCham
PublisherSpringer
Pages128-136
ISBN (Electronic)978-3-030-32254-0
ISBN (Print)978-3-030-32253-3
DOIs
StatePublished - 2019
Event22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019) - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
https://www.miccai2019.org/

Publication series

NameLecture Notes in Computer Science
Volume11768
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019)
Abbreviated titleMICCAI 2019
CountryChina
CityShenzhen
Period13/10/1917/10/19
Internet address

Fingerprint

Masks
Dentistry
Computer aided design
Imaging techniques
Experiments

Cite this

Ghazvinian Zanjani, F., Anssari Moin, D., Claessen, F., Cherici, T., Parinussa, S., Pourtaherian, A., ... de With, P. H. N. (2019). Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. In D. Shen, T. Liu, & T. M. Peters (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V (pp. 128-136). (Lecture Notes in Computer Science; Vol. 11768). Cham: Springer. DOI: 10.1007/978-3-030-32254-0_15
Ghazvinian Zanjani, Farhad ; Anssari Moin, David ; Claessen, Frank ; Cherici, Teo ; Parinussa, Sarah ; Pourtaherian, Arash ; Zinger, Sveta ; de With, Peter H.N./ Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. editor / Dinggang Shen ; Tianming Liu ; Terry M. Peters. Cham : Springer, 2019. pp. 128-136 (Lecture Notes in Computer Science).
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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.",
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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, T Liu & TM Peters (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. Lecture Notes in Computer Science, vol. 11768, Springer, Cham, pp. 128-136, 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, (MICCAI2019), Shenzhen, China, 13/10/19. DOI: 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 ; Cherici, Teo ; Parinussa, Sarah ; Pourtaherian, Arash; Zinger, Sveta; de With, Peter H.N.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. ed. / Dinggang Shen; Tianming Liu; Terry M. Peters. Cham : Springer, 2019. p. 128-136 (Lecture Notes in Computer Science; Vol. 11768).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.

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M3 - Conference contribution

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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, Liu T, Peters TM, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V. Cham: Springer. 2019. p. 128-136. (Lecture Notes in Computer Science). Available from, DOI: 10.1007/978-3-030-32254-0_15