  Model-based reconstruction employing the time separation technique (TST) was
found to improve dynamic perfusion imaging of the liver using C-arm cone-beam
computed tomography (CBCT). To apply TST using prior knowledge extracted from
CT perfusion data, the liver should be accurately segmented from the CT scans.
Reconstructions of primary and model-based CBCT data need to be segmented for
proper visualisation and interpretation of perfusion maps. This research
proposes Turbolift learning, which trains a modified version of the multi-scale
Attention UNet on different liver segmentation tasks serially, following the
order of the trainings CT, CBCT, CBCT TST - making the previous trainings act
as pre-training stages for the subsequent ones - addressing the problem of
limited number of datasets for training. For the final task of liver
segmentation from CBCT TST, the proposed method achieved an overall Dice scores
of 0.874$\pm$0.031 and 0.905$\pm$0.007 in 6-fold and 4-fold cross-validation
experiments, respectively - securing statistically significant improvements
over the model, which was trained only for that task. Experiments revealed that
Turbolift not only improves the overall performance of the model but also makes
it robust against artefacts originating from the embolisation materials and
truncation artefacts. Additionally, in-depth analyses confirmed the order of
the segmentation tasks. This paper shows the potential of segmenting the liver
from CT, CBCT, and CBCT TST, learning from the available limited training data,
which can possibly be used in the future for the visualisation and evaluation
of the perfusion maps for the treatment evaluation of liver diseases.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics