Potentially it might also serve for diagnosing liver diseases. The experimental C-arm CBCTp Computed Tomography (CT) perfusion or CTp imaging is a scanning protocol of the liver consists of multiple bidirectional method that can be used for the diagnosis and treatment planning rotations with pauses in between (Datta et al., 2017), which, of liver tumours. C-arm cone-beam CT, referred to here in combined with slow rotation, results in a very limited number short as CBCT, on the other hand, can be advantageous during of projections. A simplified approach would be to reconstruct interventions as the acquisitions can be done without moving every rotation separately, the straightforward approach, the patient due to the availability of CBCT as a part of the interventional which can result in over or underestimation of perfusion parameters suites (Orth et al., 2008). It has been shown that (Haseljić et al., 2021). Recent publications have shown CBCT perfusion maps of the brain would not be inferior to that model-based reconstruction and time separation technique the CT perfusion maps (Niu et al., 2016), and when CT perfusion (TST) could deal with poor temporal resolution (Montes and scans are acquired soon enough, it could the patient's Lauritsch, 2009; Neukirchen et al., 2010; Manhart et al., 2013; life (Powers et al., 2019). C-arm CBCT perfusion (CBCTp) Bannasch et al., 2018; Kulvait et al., 2022; Haseljić et al., 2021, imaging of the liver could allow inspection and evaluation of 2022) and provide highly accurate liver perfusion maps.

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.