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Mask-MCNet: Instance Segmentation in 3D Point Cloud of Intra-oral Scans

  • Farhad Ghazvinian ZanjaniEmail author
  • David Anssari Moin
  • Frank Claessen
  • Teo Cherici
  • Sarah Parinussa
  • Arash Pourtaherian
  • Svitlana Zinger
  • Peter H. N. de With
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

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

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

Supplementary material

490279_1_En_15_MOESM1_ESM.pdf (3.6 mb)
Supplementary material 1 (pdf 3708 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Farhad Ghazvinian Zanjani
    • 1
    • 2
    Email author
  • David Anssari Moin
    • 2
  • Frank Claessen
    • 2
  • Teo Cherici
    • 2
  • Sarah Parinussa
    • 2
  • Arash Pourtaherian
    • 1
  • Svitlana Zinger
    • 1
  • Peter H. N. de With
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Promaton Co., Ltd.AmsterdamThe Netherlands

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