Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874$\pm$0.031 and 0.905$\pm$0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.

翻译：采用时间分离技术（TST）的模型重建方法被证明可改善基于C臂锥束计算机断层扫描（CBCT）的肝脏动态灌注成像。为利用从CT灌注数据提取的先验知识应用TST，需从CT扫描中精确分割肝脏。对原始及模型重建后的CBCT数据进行分割，是实现灌注图可视化与解读的必要前提。本研究提出Turbolift学习法，该方法按CT、CBCT、CBCT TST的训练顺序，在多个肝脏分割任务上依次训练改进版多尺度注意力UNet，使前序训练成为后续任务的预训练阶段，从而解决训练数据集数量有限的问题。在CBCT TST肝脏分割的最终任务中，所提方法在6折和4折交叉验证实验中分别取得0.874±0.031和0.905±0.007的整体Dice分数——相较于仅针对该任务训练的模型实现统计显著性改进。实验表明，Turbolift不仅提升模型整体性能，还能增强其对栓塞材料伪影及截断伪影的鲁棒性。此外，深入分析验证了分割任务的顺序合理性。本文展示了利用有限训练数据从CT、CBCT及CBCT TST中分割肝脏的潜力，该成果未来有望用于肝脏疾病治疗评估中灌注图的可视化与评价。