- To start a collaboration at SPL and learn about Slicer
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- Measuring topological variations, especially around cancer tissues, could provide effective medicine and cancer treatment. Computational tools on volume data sets and algorithms implemented 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. Caveat: 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 minimal effect in 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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- It is easier to learn from tutorials and they seemed to work quickly -- they are easy to follow. 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.
From the commonGL point of view, some other site to consider areL 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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