Shape Analysis: Description &Framework Develop a generally applicable description for statistical shape analysis studies, as well as a computational framework.

Slides:

Advertisements

Similar presentations

NA-MIC National Alliance for Medical Image Computing National Alliance for Medical Image Computing: NAMIC Ron Kikinis, M.D.

Advertisements

0-1 Team # Status Report (1 of 4) Client Contact –Point 1 –Point 2 Team Meetings –Point 1 –Point 2 Team Organization –Point 1 –Point 2 Team #: Team Name.

0-1 Team # Status Report (1 of 4) Client Contact –Status Point 1 –Status Point 2 Team Meetings –Status Point 1 –Status Point 2 Team Organization –Description.

Corpus Callosum Segmentation Tool Project Martin Styner Department of Computer Science & Psychiatry Neuro Image Analysis Laboratory.

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Quantitative Analysis of Diffusion Tensor Measurements along White Matter Tracts Postdoctoral fellow,

Software Process for Distributed Teams KITWARE, Inc.

William Lorensen GE Research Niskayuna, NY February 12, 2001 Insight Segmentation and Registration Toolkit.

National Alliance for Medical Image Computing – Algorithms Core (C1a) Five investigators: –A. Tannenbaum (BU), P. Golland (MIT), M. Styner.

NA-MIC National Alliance for Medical Image Computing NAMIC Engineering Activities September 8, 2005.

© What do bioinformaticians do?

NA-MIC National Alliance for Medical Image Computing NA-MIC Software Engineering Bill Lorensen GE Research NA-MIC Engineering Core PI.

Proposal for Term Project Operating Systems, Fall 2015 J. H. Wang Sep. 18, 2015.

Morphometry BIRN Bruce Rosen, M.D. Ph.D.. Scientific Goal Methods –Multi-site MRI calibration, acquisition –Integrate advanced image analysis and visualization.

NA-MIC National Alliance for Medical Image Computing Core 1 & Core 3 Projects.

NA-MIC National Alliance for Medical Image Computing Shape Analysis and Cortical Correspondence Martin Styner Core 1 (Algorithms), UNC.

National Alliance for Medical Image Computing Project Week June 26-30, 2006 Participants: 56 NA-MIC (38), Collaborators (18) –NA-MIC:

National Alliance for Medical Image Computing Programming Half-Week January 2006 Clinical/End-User Applications Rule Based Segmentation.

Algorithms Organization Ross Whitaker – Utah Polina Golland – MIT (Kayhan B.) Guido Gerig – Utah Martin Styner – UNC Allen Tannenbaum – Georgia Tech.

NA-MIC National Alliance for Medical Image Computing AHM: Dissemination Update Tina Kapur, Ph.D., Co-PI Steve Pieper, Ph.D., Co-PI.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing National Alliance for Medical Image Computing: NAMIC Ron Kikinis, M.D.

Visualization and Computer Vision GE Research Niskayuna, NY.

The NDAR Model for Publishing Findings in the Life Sciences Dan Hall Manager, National Database for Autism Research, NIMH.

NA-MIC National Alliance for Medical Image Computing UNC Core 1: What did we do for NA-MIC and/or what did NA-MIC do for us Guido Gerig,

OHT 15.1 Galin, SQA from theory to implementation © Pearson Education Limited 2004 Templates The contribution of templates to software quality The organizational.

NA-MIC National Alliance for Medical Image Computing NA-MIC UNC Guido Gerig, Martin Styner, Isabelle Corouge

NA-MIC National Alliance for Medical Image Computing Core 1b – Engineering Computational Platform Jim Miller GE Research.

Aerial Image Exploitation Change Detection Event Detection Object Tracking Region Classification Medical Image Analysis Automated Cancer Detection Automatic.

NA-MIC National Alliance for Medical Image Computing AHM 2006 Continuing Discussion of Validation.

National Alliance for Medical Image Computing Utah DTI Research Differential Geometry for DTI analysis Descriptive statistics of DTI.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

Core 1 Introduction Overall structure Groups/investigators Algorithms and engineering Algorithms goals and DBPs Aims and preliminary results.

NA-MIC National Alliance for Medical Image Computing Programming Week Kickoff MIT, June 27, 2005.

NA-MIC National Alliance for Medical Image Computing NAMIC Core 3.1 Overview: Harvard/BWH and Dartmouth Structural and Functional Connectivity.

QNT 351 Week 2 DQ 4 Why it is important to find the shape of data distribution before computing descriptive statistics? Do all variables follow normal.

EDL 510 Week 6 Learning Team Global Service Learning Project Proposal To purchase this material click below link

HRM 319 Week 1 DQ 2 What is the goal of implementing HR technology, such as HR portals or HR software? How can these goals and objectives be accomplished?

Supporting quality devices

Neuroimage Analysis Center

2007 Project Half Week ITK Registration Parallelization: Develop implementations of ITK registration methods to take advantage of multi-core and multi-processor.

Riemannian DTI Filters: Develop algorithms and ITK modules for basic image processing on tensor fields using Riemannian approaches. Team Plan/Expected.

Team Plan/Expected Challenges

Core 2 Progress Day 1 Salt Lake City

DLPFC Semi-automatic Segmentor: With minimal user interaction, this rule-based algorithm will segment the DLPFC from an input volume. This is a Slicer.

How To Add A Xerox Printer To Google Chrome

Frio River Cabins - Frio Vacation Homes - Frio Country Resort

Team 1: 32 responses Team 2: 55 responses Team 3: 29 responses

These two sections should be completed by Jan 31, 2006.

Algorithmic Problem Solving: Programming with Processing IS 101Y/CMSC 101 Computational Thinking and Design Thursday, September 12, 2013 Carolyn Seaman.

Tobias Heimann - DKFZ Ipek Oguz - UNC Ivo Wolf - DKFZ

Scripts to combine UNC Shape Analysis with Spherical Wavelet Features

Community Participation

Partner Implications from Seminar: New Approaches to Capacity Development Break-out Group: Role of Assessment frameworks.

(team representative name here)

2007 Project Half Week Transition of Slicer2.x Modules to Slicer3: FreeSurfer Integration of FreeSurfer scalar overlays and colour lookup tables into Slicer3.

Transition of Slicer2.x Modules to Slicer3: Fiducials

Utah Algorithms Progress and Future Work

Mapped Hexahedral Meshing: Evaluate the use of multi-resolution itk::fem registration for mapped meshing Team Plan/Expected Challenges/Publication Ritesh.

Grid Interface for Slicer3: Make Slicer 3 work on a set of clusters.

2006 Summer PrWeek Slicer3 for Image Guided Therapy (IGT): Convert vtkMRAblation and vtkFMRIEngine to Slicer3 modules. Team Plan/Expected Challenges/Publication.

LONI Pipeline Integration/ UNC shape analysis

Slicer Timeline GUI Thread Processing Thread

Firebase Vs. MongoDB: Choose the Best Database of 2019

2007 Project Half Week Transition of Slicer2.x Modules to Slicer3: ModelMaker Extend ModelMaker to use MRML Color nodes. Team Plan/Expected Challenges/Publication.

Mesh Untangling: 2006 Summer PrWeek Team

2006 Summer PrWeek Correspondence Based on Local Curvature: Developing methods in ITK for using local curvature features to establish population based.

Presentation transcript:

Shape Analysis: Description &Framework Develop a generally applicable description for statistical shape analysis studies, as well as a computational framework. Team Plan/Expected Challenges Algorithms: Algorithms for DTI shape analysis in the proposed framework. Tom Fletcher, Utah (Algorithms) Martin Styner UNC (software) (contact) Jim Miller, GE (software) Software: Design and implement the description for statistical shape studies, including populations, subjects and objects. Use in combintaion with a database and pipeline framework. Clinical: Shape analysis of DTI data Accomplished by end of Programming Week Clear design for statistical studies and experiments using DART2 database system and LONI pipeline. Design of database API. Next step is implementation of database system.