Difference between revisions of "Collaboration/Iowa/Meshing/Migrate Iowa Neural Net code to pure ITK"
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<h1>Progress</h1> | <h1>Progress</h1> | ||
| + | Two milestones were reached in this effort. | ||
| + | *The first was that we have integrated a rigid body initialization for a Thirion Demons registration for atlas <-> subject registration. The above modification was used to warp the atlas to the subject as shown in the figures. | ||
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| + | *The second was that the neural network code now supports all ITK transforms for registration of the atlas<->subject. This was achieved using the itkTransformReader and the a templated function over the image type and transform type to resample the images. | ||
</div> | </div> | ||
Revision as of 02:55, 29 June 2007
Home < Collaboration < Iowa < Meshing < Migrate Iowa Neural Net code to pure ITK2007_Programming/Project_Week_MIT
Key Investigators
- Iowa: Vincent Magnotta and Nicole Grosland
- Kitware: Stephen Alyward
Objective
Conversion of the Iowa Neural netwok segmentation using a flexible module for image registration supporting all ITK transforms written using the transform I/O in ITK. Conversion to the ITK neural network from the current annie implementation may also be
Approach, Plan
Integrate additional ITK transform types into the neural network using the general ITK transform I/O mechanism. Evaluate the changes to segment the phalanx bones bones of the hand.
Progress
Two milestones were reached in this effort.
- The first was that we have integrated a rigid body initialization for a Thirion Demons registration for atlas <-> subject registration. The above modification was used to warp the atlas to the subject as shown in the figures.
- The second was that the neural network code now supports all ITK transforms for registration of the atlas<->subject. This was achieved using the itkTransformReader and the a templated function over the image type and transform type to resample the images.
References
- Magnotta VA, Heckel D, Andreasen NC, Cizadlo T, Corson PW, Ehrhardt JC, Yuh WT. "Measurement of brain structures with artificial neural networks: two- and three-dimensional applications", Radiology 211 (1999), 781-90.
- Powell S, Magnotta VA, Johnson HJ, Jammalamadaka VK, Andreasen NC. "Registration and Machine Learning Based Automated Segmentation of Subcortical and Cerebellar Brain Structures", NeuroImage, In Press.
