- Deploy Deep Learning Networks in Slicer for the purpose of segmenting vertebral bodies and object recognition
- Investigate different approaches for need specific deployment of deep learning networks.
- CPP API
- tfdeploy via pure python
- Implement a method for deployment of deep learning methods
- Investigated a number of modules that use deep learning on the advice of Andras
- It appears that cross-platform deployment of deep learning networks implemented in tensor flow can be deployable using the methods similar to the ShapeVariationAnalyser
Python 2.7.13 (default, Jul 18 2017, 23:29:12) [MSC v.1800 64 bit (AMD64)] on win32
>>> shapeVariationAnalyzerLogic = slicer.modules.shapevariationanalyzer.logic()
>>> import ShapeVariationAnalyzer
>>> shapeVariationAnalyzerLogicCast = ShapeVariationAnalyzer.ShapeVariationAnalyzerLogic( shapeVariationAnalyzerLogic)
- I will need the ability to load pre-trained networks, which may be exposed but I cannot tell at this time.
- I spent some time learning how to SuperBuild Modules in an effort to enhance the ease with which these and other modules I develop can be maintained and deployed. I focused on a simpler example. My superbuild extension is a wrapping of VCG functionality into Slicer. VCG is used to enhance surface quality. It underlies much functionality in slicer.