Purpose: The aim of this paper is to provide a novel, publicly available standard image dataset with a useful evaluation framework for assessing nonrigid two-/three-dimensional (2D/3D) registration algorithms.
Acquisition and validation methods: A pig lung model was used to obtain the image dataset. Inflated with different amounts of oxygen, a sequence of 3D volume data was acquired with computed tomography (CT), which ideally simulated different respiratory phases. With the model inflated and kept in certain states, 3D CT, 2D CT scout image and 2D x-ray were acquired for the same respiratory phases, making them suitable to establish the evaluation dataset for 2D/3D registration algorithms. A total of 120 well-distributed landmarks in every 3D volume were manually annotated and checked by several radiologists using semi-automatic software to generate the dataset.
Data format and usage notes: All 3D image data were stored in both DICOM and ITK Meta format, and 2D image data were stored in DICOM format. A total of 120 landmarks were manually annotated for each 3D image. Among these landmarks, eight landmarks located on large branch of the bronchial tree were also annotated in 2D images. The landmark coordinates were stored in a text file. The detailed usage including a standard evaluation framework for the proposed dataset is also provided. The dataset can be downloaded from the Zenodo repository (https://doi.org/10.5281/zenodo.997887).
Potential applications: Our standard dataset was acquired with advanced clinical imaging devices and is quite suitable for quantitatively evaluating state-of-art, nonrigid 2D/3D registration algorithms.
Keywords: 2D/3D registration; evaluation; image-guided surgery; standard dataset.
© 2018 American Association of Physicists in Medicine.