Quantification of 3d tooth movement after a segmentation using a Watershed 3d method

Haddani, Hajar; Elmoutaouakkil, Abdlmejid; Benzekri, Fatiha; Aghoutan, Hakima; Bourzgui, Farid

doi:10.1109/icmcs.2016.7905563

Cited by 1 publication

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“…A crucial step in orthodontic treatment is the assessment of tooth movement, which has been studied using imaging modalities that include photographic 2D images, 2D X-rays, and intraoral 3D scanners. A precursor to measuring tooth movement is the detection, segmentation or separation of data that represents individual teeth [ 1 ]. In this paper, we focus on the detection and segmentation of individual teeth in 2D photographic images.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images

et al. 2022

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The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps to achieve an autonomous system of assessment of the dentition. Methods: A dataset of 797 occlusal views from photographs of teeth was created. The photographs were manually semantically segmented and labeled. Machine learning methods were applied to identify a robust deep network architecture able to semantically segment teeth in unseen photographs. Using well-defined metrics such as accuracy, precision, and the average mean intersection over union (mIoU), four network architectures were tested: MobileUnet, AdapNet, DenseNet, and SegNet. The robustness of the trained network was additionally tested on a set of 47 image pairs of patients before and after orthodontic treatment. Results: SegNet was the most accurate network, producing 95.19% accuracy and an average mIoU value of 86.66% for the main sample and 86.2% for pre- and post-treatment images. Conclusions: Four architectural tests were developed for automated individual teeth segmentation and detection in two-dimensional photos that required no post-processing. Accuracy and robustness were best achieved with SegNet. Further research should focus on clinical applications and 3D system development.

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Section: Introductionmentioning

confidence: 99%