Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

Haseljić, Hana, Chatterjee, Soumick, Frysch, Robert, Kulvait, Vojtěch, Semshchikov, Vladimir, Hensen, Bennet, Wacker, Frank, Brüsch, Inga, Werncke, Thomas, Speck, Oliver, Nürnberger, Andreas, Rose, Georg

arXiv.org Artificial Intelligence 

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.

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