@article{Bianchi2023,

title = {Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.},

author = {Anchling L and Hutin N and Huang Y and Barone S and Roberts S and Miranda F and et al},

url = {https://doi.org/10.1007/978-3-031-45249-9_5},

doi = {10.1007/978-3-031-45249-9_5},

isbn = {978-3-031-45249-9},

year  = {2023},

date = {2023-10-09},

urldate = {2023-10-09},

journal = {Lecture Notes in Computer Science},

volume = {14242},

abstract = {Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3 and <2 mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.},

keywords = {3D CBCT scans, Deep Learning, Image processing, medical image registration, standardized orientation},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023i,

title = {Dentalmodelseg: Fully Automated Segmentation of Upper and Lower 3D Intra-Oral Surfaces.},

author = {Leclercq M and Ruellas A and Gurgel M and Yatabe M and Bianchi J and Cevidanes L and et al},

url = {https://www.semanticscholar.org/paper/FiboSeg%3A-Fully-automated-segmentation-of-upper-and-Leclercq-Ruellas/7269780afbd9c060e4509465f2b24d7fdbc35924},

year  = {2022},

date = {2022-01-01},

journal = {Semantic Scholar},

abstract = {In this paper, we present a deep learning based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as UNETs. We test our method in a dental application for segmentation of dental crowns. The neural network is trained for the multi-class segmentation, using image labels as ground truth. The segmentation task achieved an average Dice of 0.97, sensitivity of 0.97 and precision of 0.97.},

keywords = {bone segmentation, Deep Learning, dental crown, intra-oral surface, Universal label id},

pubstate = {published},

tppubtype = {article}

}

@article{Ribera2019,

title = {Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis},

author = {T R Nina and D Priscille De and Y Marilia and R Antonio and I Marcos and P Beatriz and S Martin and R G Joao and B Jonas and C Lucia and P Juan-Carlos },

url = {https://pubmed.ncbi.nlm.nih.gov/31359900/},

doi = {10.1117/12.2506018},

year  = {2019},

date = {2019-02-00},

urldate = {2019-02-00},

journal = {Dento Maxillo Facial Radiology},

volume = {10950},

abstract = {We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Sheer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology.},

keywords = {Classification, Deep Learning, Neural Network, osteoarthritis, temporomandibular joint disorders},

pubstate = {published},

tppubtype = {article}

}