  Perfusion imaging is a valuable tool for diagnosing and treatment planning
for liver tumours. The time separation technique (TST) has been successfully
used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data.
The reconstruction can be accompanied by the segmentation of the liver - for
better visualisation and for generating comprehensive perfusion maps. Recently
introduced Turbolift learning has been seen to perform well while working with
TST reconstructions, but has not been explored for the time-resolved volumes
(TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be
useful for tracking the movement of the liver over time. This research explores
this possibility by training the multi-scale attention UNet of Turbolift
learning at its third stage on the TRVs and shows the robustness of Turbolift
learning since it can even work efficiently with the TRVs, resulting in a Dice
score of 0.864$\pm$0.004.
