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:聚变成像是诊断和治疗肝脏肿瘤的宝贵工具。时间分离技术(TST)已经成功地用于模拟C-arm conne-beam计算透析(CBCT)扩散数据。在进行重建的同时,可以同时对肝脏进行分解,以更好地视觉化和绘制全面的渗入图。最近引进的涡轮起伏学习在与TST重建合作时表现良好,但对于根据TST重建估计的时间解析量(TRV)尚未进行探索。TRV的分解可用于跟踪肝脏随时间移动的情况。这一研究通过培训Turbolift在TRVs第三阶段的多级注意力Unet学习,并展示Turbolift学习的稳健性,因为Turbolift学习甚至能够有效地与TRVs合作,结果是0.864美元/pm0.004的Dice分数。