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. 2019 Mar 7;9:3840. doi: 10.1038/s41598-019-40414-y

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Examples of annotations processing after each stage: (Stage 1) annotated by the trained faster R-CNN, there were certain overlapping boxes (a); (Stage 2) after deleting the overlap boxes with lower scores, certain labels of teeth number were incorrect because the neural network confused them with other similar teeth; (Stage 3) the teeth number labels were matched with template for correction, but errors were induced when there were missing teeth (in films 1 and 3), however, the gap between adjacent teeth boxes (stage 2). (b) could be treated as a feature to predict the missing teeth; (Stage 4) after inserting the predicted missing teeth and matching with template from (Stage 2), the labels of teeth number were corrected.