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Registration Case Library. Main Mission: Make “Registration Life” easier. Objectives: build a comprehensive library of registration case scenarios give users an educated starting point for their own individual registration problem
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Registration Case Library • Main Mission: Make “Registration Life” easier. • Objectives: • build a comprehensive library of registration case scenarios • give users an educated starting point for their own individual registration problem • avoid excessive repeats in a trial- and error parameter exploration • educate user community about the particulars of Slicer-based registration • educate user community about basics of image registration
Call For Datasets "if you have a registration problem that is not yet covered in our library, send us your case: we will post it along with our best registration solution/strategy. If you agree to the posting of the anonymized image data, you get a free registration, the user community gets a new example case. Everybody wins.” • What We Will Do • seek the best possible registration obtainable with the most recent version of 3DSlicer • post the anonymized image as a new case in our Slicer Registration Case Library • post the exact workflow used to obtain the shown solution registration will be posted alongside the data as a guided step-by-step tutorial • the parameters for successful registration will also be posted as a loadable custom "Registration Preset" file that you can load directly into Slicer and apply on your data • if you can provide us with fiducial pairs or other criteria that define a good registration, we will use them in optimization efforts. • the registration objective & background, main challenges and strategy recommendations will be posted • an acknowledgment of your lab as the data source is posted, if desired with a link to your institution and/or related research papers
For most, registration is a preprocessing step, not a destination, hence evaluation interest will be chiefly in fast (qualitative) manner Hence “Visualization” is first-used method of choice. exception: sensitivity analyses of pipelines. Result Evaluation
Result Visualization 1. Slicer View Toggle Button & Fade fade toggle
Result Visualization 2. Animated GIFs
Result Visualization 3. Color Overlay
Result Visualization 4. Checkerboard
Result Visualization 5. Subtraction/Ratio/Var Image blendingmode
Result Visualization 6. Label Maps Visualize Overlap viaHausdorff Distance Module
Result Visualization 7. Volume Rendering dual rendering: GPU restrictions may apply 8. Surface Model Rendering requires segmentation of each set
Registration Parameters Rigid - Affine DOF: (3,6,7,9,12) Multi-resolution: (2x, 4x, 8x, 16x) Cost function: (MI, NC, MSq, IR) Mask Image: (Labelmap) Mask ROI Box: (ROI node) Initialization: (none, image centers, moments) Histogram Mask: (excl. intensity range)
Fiducial RMS ROI image similarity Segmentation Overlap Global Cost Function Result Quantitative Result Assessment