E Model Registration

[Melany Odeh]

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Goal of registration is to align position and orientation of images, models, and other objects in 3D space. 3D Slicer offers many registration tools, this page only lists those that are most commonly used.

Advantage of this approach is that it is simple, applicable to any data type, and approximate alignment can be reached very quickly. However, achieving accurate registration using this approach is tedious and time-consuming, because many fine adjustments steps are needed, with visual checks in multiple orientations after each adjustment.

Fiducial registration wizard (in SlicerIGT extension): for registering any data nodes (even mixed data, such as registration of images to models), and for images that are not aligned at all. Supports rigid and deformable registration, automatic point matching, automatic collection from tracked pointer devices. See U-12 SlicerIGT tutorial for a quick introduction of main features.

Grayscale images can be automatically aligned to each other using intensity-based registration methods. If an image does not show up in the input image selector then most likely it is a color image, which can be converted to grayscale using Vector to scalar volume module.

Intensity-based image registration methods require reasonable initial alignment, typically less than a few centimeter translation and less than 10-20 degrees rotation error. Some registration methods can perform initial position alignment (e.g., using center of gravity) and orientation alignment (e.g., matching moments). If automatic alignment is not robust then manual or semi-automatic registration methods can be used as a first step.

It is highly recommended to crop the input images to cover approximately the same anatomical region. This allows faster and much more robust registration. Images can be cropped using Crop volume module.

General Registration (ANTs) (in SlicerANTS extension): Similarly to Elastix, default parameter set should work well for most image registration tasks. The module also exposes many registration parameters that users can tweak.

Sequence registration: Automatic 4D image (3D image time sequence) registration using Elastix. Can be used for tracking position and shape changes of structures in time, or for motion compensation (register all time points to a selected time point).

Registration of segmentation and binary images are very different from grayscale images, as only the boundaries can guide the alignment process. Therefore, general image registration methods are not applicable to binary images.

Segment registration (in SegmentRegistration extension): registers a selected pair of segments fully automatically. Supports rigid, affine, and deformable registration. Binary images can be registered by converting to segmentation nodes first.

Model registration (in SlicerIGT extension): uses iterative closest points. this method tends to get stuck in a local optimum, therefore it is important to start it from a good initial position (e.g., computed using manual or semi-automatic registration).

FastModelAlign automatic surface registration method in SlicerMorph extension: more robust (can converge from farther initial registration error, has higher chance of finding global optimum) than iterative closest point based algorithms. Allows rigid (with or without scaling) and affine registration of models.

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Are there any caveats to the method or things people should keep in mind when choosing regions? Perhaps a trade-off in size of chosen region vs registration time? Are disconnected regions OK or must the regions to be registered be a single connected region?

I would choose a little bit of a well-matched region to work as pivot, and then the rest of the selection would be badly matched parts. The idea in my mind is trying to set up a context that would push or rotate the moving-model to the correct place considering points of both models will try to be as close as possible as they can. For a deeper idea of this, I think you should study how the algorithm works, I think I just have a general idea of it.

This videotutorial show how to make a rigid registration of two models corresponding to the same anatomy on different subjects so that the selected region of one model matchs a similar region on the other model (no entire model matching is done, only region of interest is accounted for registration)

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Landmark registration is simple, always guaranteed to work, but requires manual definition of anatomical landmarks. I would recommend Fiducial Registration Wizard module (provided by SlicerIGT extension). You can use landmark registration as an approximate initial step for automatic registrations.

For rigid registration, you may choose ICP-based surface registration (SlicerIGT extension, Surface registration module). It is very accurate but requires good initial alignment, otherwise it may get stuck in local optimum.

The approaches described above should work. If you need help then describe what you did exactly (list of every mouse click and a few screenshots; alternatively, you can post a screen capture video), what did you expect to happen, and what happened instead.

In my perspective on Landmark Registration, such as with FiducialWizard, you need two groups of landmarks: one from the model or volume you want to move and the other from the model or volume that remains fixed. The module will then calculate the transform that describes the relationship between these two groups of landmarks. Once you have this transform, you apply it to the model or volume that you want to move, which will align it with the fixed model or volume.

This video may help you: Align 3D objects using landmarks in 3D Slicer - YouTube

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Purpose: : Augmented Reality (AR) in Laparoscopic Liver Resection requires anatomical landmarks and the silhouette to be found on the laparoscopic image. They are used to register the preoperative 3D model obtained from CT segmentation. The existing AR systems rely on the surgeon to 1) annotate the landmarks and silhouette and 2) provide an initial registration. These non-trivial tasks require surgeon attention which may perturb the procedure. We propose methods to solve both tasks, hence registration, automatically.

Methods: : The landmarks are the lower ridge and the falciform ligament. We solve 1) by training a U-Net from a new dataset of 1415 labelled images extracted from 68 procedures. We solve 2) by a novel automatic coarse-to-fine pose estimation method, including visibility-reasoning within an iterative robust process. In addition, we propose to divide the ridge into six anatomical sub-parts, making its annotation and use in registration more accurate.

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