dcbia-ortholab Goto Github PK

3dteethseg22_challenge

Dental model seg challenge docker repository build

ali_cbct

Automatic landmark identification in CBCT scans

ali_ios

Automatic Landmark Identification in Intra Oral scans

amasss_cbct

Automatic segmentation of CBCT scans with a 3D Unet

angleplanes-extension

aso

Automatic standardized orientation

cbct_seg

CBCT segmentation with image processing and machine learning approaches

cl3ft

Cleft analysis tools

cmfreg

Cranio-Maxillofacial registration

databaseinteractorextension

diagnosticindexextension

easyclip-extension

fly-by-cnn

Acquire features from a 3D object using a ray-cast approach.

intensitysegmenter

Dental Tools IntensitySegmenter

jwt-user-login

JSON Web Tokens (JWT) user login form front end package

meshstatisticsextension

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.

mfsda_python

Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python 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, biological 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.

pickandpaintextension

q3dcextension

shapeaxi

Shape Analysis Explainability And Interpretability

shapequantifierextension

shapevariationanalyzer

Shape modeling and classification, extract shape features

slicerautomateddentaltools

A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS

slicerbatchanonymize

Slicer extension to anonymize a batch of DICOM images.

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/

slicercmf-docker

This repository will contain the docker files that containerize Slicer CLIs and will be accessible through dsci

slicercmf.github.io

Infrastructure for generating the cmf.slicer.org website is available at https://github.com/slicercmf/slicercmf.github.io

slicerconda

The "Conda Path Manager" for Slicer simplifies Miniconda integration by managing installation paths and providing tools for executing Conda commands.

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.