MeshSNet: Deep Multi-scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces

Chunfeng Lian, Li Wang, Tai Hsien Wu, Mingxia Liu, Francisca Durán, Ching Chang Ko, Dinggang Shen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    35 Citations (Scopus)

    Abstract

    Accurate tooth labeling on 3D dental surfaces is a vital task in computer-aided orthodontic treatment planning. Existing automated or semi-automated methods usually require human interactions, which is time-consuming. Also, they typically use simple geometric properties as the criteria for segmentation, which cannot well handle the high variation of tooth appearance across different patients. Recently, several pioneering deep neural networks (e.g., PointNet) have been proposed in the computer vision and computer graphics communities to efficiently segment 3D shapes in an end-to-end manner. However, these methods do not perform well in our specific task of tooth labeling, especially considering that they cannot explicitly model fine-grained local geometric context of teeth (although only a small portion of dental surfaces but with different shapes and appearances). In this paper, we propose a specific deep neural network (called MeshSNet) for end-to-end tooth segmentation on 3D dental surfaces captured by advanced intraoral scanners. Using directly raw mesh data as input, our MeshSNet adopts novel graph-constrained learning modules to hierarchically extract multi-scale contextual features, and then densely integrates local-to-global geometric features to comprehensively characterize mesh cells for the segmentation task. We evaluated our proposed method on an in-house clinic dataset via 3-fold cross-validation. The experimental results demonstrate the superior performance of our MeshSNet method, compared with the state-of-the-art deep learning methods for 3D shape segmentation.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
    EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages837-845
    Number of pages9
    ISBN (Print)9783030322250
    DOIs
    Publication statusPublished - 2019
    Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
    Duration: 2019 Oct 132019 Oct 17

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11769 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
    Country/TerritoryChina
    CityShenzhen
    Period19/10/1319/10/17

    Bibliographical note

    Publisher Copyright:
    © 2019, Springer Nature Switzerland AG.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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