1. Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans
- Author
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Ghazvinian Zanjani, Farhad, Anssari Moin, David, Claessen, Frank, Cherici, Teo, Parinussa, Sarah, Pourtaherian, Arash, Zinger, Sveta, de With, Peter H.N., Shen, Dinggang, Yap, Pew-Thian, Liu, Tianming, Peters, Terry M., Khan, Ali, Staib, Lawrence H., Essert, Caroline, Zhou, Sean, Video Coding & Architectures, Center for Care & Cure Technology Eindhoven, Signal Processing Systems, and Biomedical Diagnostics Lab
- Subjects
Computer science ,business.industry ,Deep learning ,Monte Carlo method ,Point cloud ,020207 software engineering ,02 engineering and technology ,010501 environmental sciences ,Object (computer science) ,01 natural sciences ,3D point cloud ,Minimum bounding box ,0202 electrical engineering, electronic engineering, information engineering ,Instance segmentation ,Segmentation ,Computer vision ,Polygon mesh ,Artificial intelligence ,Intra-oral scan ,business ,0105 earth and related environmental sciences - 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.
- Published
- 2019