DCBIA-OrthoLab
Dental and Craniofacial Bionetwork for Image Analysis (DCBIA)
University Of Michigan - School of Dentistry
Pinned Repositories
3DTeethSeg22_challenge
Dental model seg challenge docker repository build
CMFreg
Cranio-Maxillofacial registration
Fly-by-CNN
Acquire features from a 3D object using a ray-cast approach.
Q3DCExtension
ShapeAXI
Shape Analysis Explainability And Interpretability
ShapeVariationAnalyzer
Shape modeling and classification, extract shape features
SlicerAutomatedDentalTools
A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS
SlicerCMF
SlicerCMF is the dissemination vehicle of powerful dental image analysis methodology based on 3D Slicer open-source software. SlicerCMF supports patient-specific decision making and assessment of the disease progression via registration of serial images. http://cmf.slicer.org/
SlicerDentalModelSeg
This extension aims to provide a GUI for a deep-learning automated teeth segmentation tool that we developed at the University of North Carolina in Chapel Hill in collaboration with the University of Michigan in Ann Arbor.
Smart-DOC
DCBIA-OrthoLab's Repositories
DCBIA-OrthoLab/IntensitySegmenter
Dental Tools IntensitySegmenter
DCBIA-OrthoLab/jwt-user-login
JSON Web Tokens (JWT) user login form front end package
DCBIA-OrthoLab/ShapeQuantifierExtension
DCBIA-OrthoLab/slicercmf.github.io
Infrastructure for generating the cmf.slicer.org website is available at https://github.com/slicercmf/slicercmf.github.io
DCBIA-OrthoLab/CBCT_seg
CBCT segmentation with image processing and machine learning approaches
DCBIA-OrthoLab/DiagnosticIndexExtension
DCBIA-OrthoLab/MFSDA
Multivariate Functional Shape Data Analysis (MFSDA) is a Matlab based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.