  Purpose: Evaluating deformable image registration (DIR) algorithms is vital
for enhancing algorithm performance and gaining clinical acceptance. However,
there's a notable lack of dependable DIR benchmark datasets for assessing DIR
performance except for lung images. To address this gap, we aim to introduce
our comprehensive liver computed tomography (CT) DIR landmark dataset library.
  Acquisition and Validation Methods: Thirty CT liver image pairs were acquired
from several publicly available image archives as well as authors' institutions
under institutional review board approval. The images were processed with a
semi-automatic procedure to generate landmark pairs: 1) for each case, liver
vessels were automatically segmented on one image; 2) landmarks were
automatically detected at vessel bifurcations; 3) corresponding landmarks in
the second image were placed using the deformable image registration method; 4)
manual validation was applied to reject outliers and confirm the landmarks'
positional accuracy. This workflow resulted in an average of ~68 landmark pairs
per image pair, in a total of 2028 landmarks for all 30 cases. The general
landmarking accuracy of this procedure was evaluated using digital phantoms.
Estimates of the mean and standard deviation of landmark pair target
registration errors (TRE) on digital phantoms were 0.64 and 0.40 mm. 99% of
landmark pairs had TREs below 2 mm.
  Data Format and Usage Notes: All data are publicly available at Zenodo.
Instructions for using our data and MATLAB code can be found on our GitHub
page.
  Potential Applications: The landmark dataset generated in this work is the
first collection of large-scale liver CT DIR landmarks prepared on real patient
images. This dataset can provide researchers with a dense set of ground truth
benchmarks for the quantitative evaluation of DIR algorithms within the liver.
