2019-05-24-ghazvinian-zanjani19a.md

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Contributed Papers	Deep Learning Approach to Semantic Segmentation in 3D Point Cloud Intra-oral Scans of Teeth	Accurate segmentation of data, derived from intra-oral scans (IOS), is a crucial step in a computer-aided design (CAD) system for many clinical tasks, such as implantology and orthodontics in modern dentistry. In order to reach the highest possible quality, a segmentation model may process a point cloud derived from an IOS in its highest available spatial resolution, especially for performing a valid analysis in finely detailed regions such as the curvatures in border lines between two teeth. In this paper, we propose an end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS. By introducing a non-uniform resampling technique, our proposed model is trained and deployed on the highest available spatial resolution where it learns the local fine details along with the global coarse structure of IOS. Furthermore, the point-wise cross-entropy loss for semantic segmentation of a point cloud is an ill-posed problem, since the relative geometrical structures between the instances (e.g. the teeth) are not formulated. By training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch, we improve the segmentation results considerably. Hence, a heavy post-processing stage for relational and dependency modeling (e.g. iterative energy minimization of a constructed graph) is not required anymore. Our experiments show that the proposed approach improves the performance of our baseline network and outperforms the state-of-the-art networks by achieving $0.94$ IOU score.	inproceedings	Proceedings of Machine Learning Research	ghazvinian-zanjani19a	0	Deep Learning Approach to Semantic Segmentation in 3D Point Cloud Intra-oral Scans of Teeth	557	571	557-571	557	false	{Ghazvinian Zanjani}, Farhad and {Anssari Moin}, David and {Verheij}, Bas and {Claessen}, Frank and {Cherici}, Teo and {Tan}, Tao and {de With}, {Peter H. N.}	given family Farhad Ghazvinian Zanjani given family David Anssari Moin given family Bas Verheij given family Frank Claessen given family Teo Cherici given family Tao Tan given family Peter H. N. de With	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/ghazvinian-zanjani19a/ghazvinian-zanjani19a.pdf