Difference between revisions of "2016 Winter Project Week/Projects/ExploringCollaborationVisualization"

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* Measuring topological variations, especially around cancer tissues, could provide effective medicine and cancer treatment.  What I learnt quickly was that, from my point of view, computational tools on volume data sets and algorithms implementing into existing platforms such as slicer (3DSlicer.org) can prove useful if they can help with margins during the operations so that only those cells which needed to be out are taken out, not less not more.  Dynamically measuring the topological and anatomical variations of medical data sets could lead to applications such as image-guided surgery.  The challenge is that it is necessary to differentiate between cancer cell and healthy tissue, and because of the technology both false positive and negative cases have been observed along with deformations, which in volume terms means that same spatial voxels are now occupied by different values.  Ofcourse, it can also mean that different tissues have the same values as well.  One of the demos in the conference clarified that the interface has minimally effect the process during operation (so as not to burden the surgeon) and also has to be better than what the surgeon is used to seeing.  
 
* Measuring topological variations, especially around cancer tissues, could provide effective medicine and cancer treatment.  What I learnt quickly was that, from my point of view, computational tools on volume data sets and algorithms implementing into existing platforms such as slicer (3DSlicer.org) can prove useful if they can help with margins during the operations so that only those cells which needed to be out are taken out, not less not more.  Dynamically measuring the topological and anatomical variations of medical data sets could lead to applications such as image-guided surgery.  The challenge is that it is necessary to differentiate between cancer cell and healthy tissue, and because of the technology both false positive and negative cases have been observed along with deformations, which in volume terms means that same spatial voxels are now occupied by different values.  Ofcourse, it can also mean that different tissues have the same values as well.  One of the demos in the conference clarified that the interface has minimally effect the process during operation (so as not to burden the surgeon) and also has to be better than what the surgeon is used to seeing.  
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From Beginner’s point of view – it is easier to learn from tutorials and then seemed to work quickly.  For example, first three tutorials are easy and provide effective guidance to run the slicer and write a simple python code as an extension.  However, if you are trying to change the basic functionality of the Slicer by implementing something deeper insider the main source code then you will have to consider what you are replacing MUST be better than what is already there – this is a hard task as the code which has made in the core of the slicer support many projects so things should not be worse than before.
 
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Finally he recommended, that I play with glslsandbox.com threeJS.org, lux renderer, 4 page of cheat-sheet for webGL as well for implementing and experimenting with initial ideas.
 
  
 
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Revision as of 14:50, 8 January 2016

Home < 2016 Winter Project Week < Projects < ExploringCollaborationVisualization

Key Investigators

  • SK Semwal, Ph.D., University of Colorado, Colorado Springs
  • Steve Pieper, Ph.D., Isomics
  • Lauren O'Donnell, MD. SPL
  • Michael Halle, Ph.D., SPL
  • Sonia Pujol, Ph.D., SPL
  • Tina Kapur, Ph.D., SPL
  • Ron Kikinis, MD, SPL

Project Description

Objective Approach and Plan Progress and Next Steps
  • To start a collaboration at SPL and learn about Slicer
  • Measuring topological variations, especially around cancer tissues, could provide effective medicine and cancer treatment. What I learnt quickly was that, from my point of view, computational tools on volume data sets and algorithms implementing into existing platforms such as slicer (3DSlicer.org) can prove useful if they can help with margins during the operations so that only those cells which needed to be out are taken out, not less not more. Dynamically measuring the topological and anatomical variations of medical data sets could lead to applications such as image-guided surgery. The challenge is that it is necessary to differentiate between cancer cell and healthy tissue, and because of the technology both false positive and negative cases have been observed along with deformations, which in volume terms means that same spatial voxels are now occupied by different values. Ofcourse, it can also mean that different tissues have the same values as well. One of the demos in the conference clarified that the interface has minimally effect the process during operation (so as not to burden the surgeon) and also has to be better than what the surgeon is used to seeing.
  • From Beginner’s point of view – it is easier to learn from tutorials and then seemed to work quickly. For example, first three tutorials are easy and provide effective guidance to run the slicer and write a simple python code as an extension. However, if you are trying to change the basic functionality of the Slicer by implementing something deeper insider the main source code then you will have to consider what you are replacing MUST be better than what is already there – this is a hard task as the code which has made in the core of the slicer support many projects so things should not be worse than before.

Finally he recommended, that I play with glslsandbox.com threeJS.org, lux renderer, 4 page of cheat-sheet for webGL as well for implementing and experimenting with initial ideas.

Background and References

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