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.

翻译：灌注成像是肝脏肿瘤诊断和治疗规划的重要工具。时间分离技术（TST）已成功应用于建模C臂锥形束计算机断层扫描（CBCT）灌注数据。在重建过程中可同步进行肝脏分割，以优化可视化效果并生成全面的灌注图谱。近期提出的Turbolift学习在处理TST重建数据时表现出色，但尚未探索其在TST重建估测的时间分辨容积（TRV）中的应用。TRV的分割有助于追踪肝脏随时间推移的运动轨迹。本研究通过在Turbolift学习第三阶段对TRV训练多尺度注意力UNet探索这一可能性，并验证了Turbolift学习的鲁棒性——即使处理TRV数据仍能高效运行，最终Dice系数达到0.864±0.004。