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

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part V
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Place of PublicationCham
PublisherSpringer
Pages128-136
Number of pages9
ISBN (Electronic)978-3-030-32254-0
ISBN (Print)978-3-030-32253-3
DOIs
Publication statusPublished - 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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11768 LNCS
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

Keywords

  • 3D point cloud
  • Deep learning
  • Instance segmentation
  • Intra-oral scan

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