This tutorial will explain how to use and customize the x-ray 2D/3D registration algorithm in an example plugin.
The screenshot above uses a Creative Commons Attribution-ShareAlike 4.0 (see https://osf.io/amh4f/ ) licensed CT volume from the 2020 VerSe Challenge (see citations below).
- Installed ImFusion SDK including the CT Plugin.
- Qt5 (at least the version that the ImFusion SDK comes with)
- CMake version 3.2 or newer
- We encourage you to build the ExamplePlugin first and to familiarize yourself with its workings.
Given a 3D volume, Example2D3DRegistrationAlgorithm class does two things:
- Compute simulated (cone-beam) X-ray images of the volume.
- Start an instance of the XRay2D3DRegistrationAlgorithm class (with custom initialization method) in order to run 2D/3D registration with the volume and the simulated X-ray images.
The initialization method used is quite basic, and builds on the keypoint initialization method defined in the XRay2D3DRegistrationInitializationKeyPoints class. It assumes that key points on the volume are known that correspond to fixed spatial locations. The coordinates of these "reference"
spatial locations are then projected onto the X-ray images. Finally, a bundle adjustment is computed in order to minimize the reprojection error of the forward-projection of the keypoints relative to the reference values.
Löffler M, Sekuboyina A, Jakob A, Grau AL, Scharr A, Husseini ME, Herbell M, Zimmer C, Baum T, Kirschke JS. A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020 https://doi.org/10.1148/ryai.2020190138.
Liebl H, Schinz D, Sekuboyina A, ..., Kirschke JS. A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data. 2021. https://arxiv.org/abs/2103.06360
Sekuboyina A, Bayat AH, Husseini ME, Löffler M, Menze BM, ..., Kirschke JS. VerSe: A Vertebrae Labelling and Segmentation Benchmark. 2021. https://arxiv.org/abs/2001.09193