ITK Release 4/A2D2 Projects/A Comprehensive Workflow for Robust Characterization of Microstructure for Cancer Studies: Difference between revisions

From KitwarePublic
Jump to navigationJump to search
(Created page with "== Title == A Comprehensive Workflow for Robust Characterization of Microstructure for Cancer Studies == Motivation == There is a growing body of cancer biology research tha...")
 
 
(2 intermediate revisions by 2 users not shown)
Line 20: Line 20:


== Deliverable ==
== 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 ==
== 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

  1. Application with python backend running slightly modified ITK filters to fit the pipeline
    • config_template.txt
    • main.py
    • convert.py
    • makeStruct.py
    • wrapper.py
  2. 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

  1. itk::ColocalizationDiceMetric -Arindam Bhattacharya
  2. itk::ColocalizationJaccardMetric -Arindam Bhattacharya

Team

  • Raghu Machiraju (Ohio State University),
  • Kun Huang