ITK Release 4/A2D2 Projects/A Comprehensive Workflow for Robust Characterization of Microstructure for Cancer Studies: Difference between revisions
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* New Application (with GUI and Viz) | * New Application (with GUI and Viz) | ||
* New classes (denoising) | * New classes (denoising) | ||
** Raghuram | |||
** | === Pipeline === | ||
# Application with python backend running slightly modified ITK filters to fit the pipeline | |||
#* config_template.txt | |||
#* main.py | |||
#* convert.py | |||
#* makeStruct.py | |||
#* wrapper.py | |||
# ITK application using already existing itk filters | |||
#* itkConfocal.cxx | |||
#* itkConfocal.h | |||
Raghuram Onti Srinivasan | |||
* First part is more flexible. Different filters and configurations can be introduced using the configuration file. | |||
* Seconc part is more rigid and has the following pipeline | |||
Image->Median filtering->Isotropic filter->Otsu Filter->Connected Components | |||
=== Colocalization === | |||
# itk::ColocalizationDiceMetric -Arindam Bhattacharya | |||
# itk::ColocalizationJaccardMetric -Arindam Bhattacharya | |||
== Team == | == Team == |
Latest revision as of 17:22, 31 August 2011
Title
A Comprehensive Workflow for Robust Characterization of Microstructure for Cancer Studies
Motivation
There is a growing body of cancer biology research that relies heavily on 3D cellular model systems. In recent years, there has been especially much interest in the tumor microenvironment (TME) that surrounds epithelial tumors. Fluorescence markers and confocal and multi-photon microscopes are employed towards the creation of the required virtual 3D annotated models of the TME.
Goals
We propose to create a comprehensive ITKv4 application that includes
- Tangible workflows for preprocessing images (denoising), and
- Segmenting, classifying and visualizing cell nuclei and their arrangements.
Emphasis will be placed on creating tangible shape spaces and tools that will associate cellular (nuclei) phenotypes to regions in the microenvironment.
We will leverage our extensive experience in developing algorithms for processing confocal stacks from thick tissue sections. The proposed tool will facilitate the use of ITKv4 among an important and growing community of cancer researchers.
Deliverable
- New Application (with GUI and Viz)
- New classes (denoising)
Pipeline
- Application with python backend running slightly modified ITK filters to fit the pipeline
- config_template.txt
- main.py
- convert.py
- makeStruct.py
- wrapper.py
- ITK application using already existing itk filters
- itkConfocal.cxx
- itkConfocal.h
Raghuram Onti Srinivasan
- First part is more flexible. Different filters and configurations can be introduced using the configuration file.
- Seconc part is more rigid and has the following pipeline
Image->Median filtering->Isotropic filter->Otsu Filter->Connected Components
Colocalization
- itk::ColocalizationDiceMetric -Arindam Bhattacharya
- itk::ColocalizationJaccardMetric -Arindam Bhattacharya
Team
- Raghu Machiraju (Ohio State University),
- Kun Huang