IEEE Trans Biomed Eng. 2021 Aug;68(8):2540-2551. doi: 10.1109/TBME.2021.3050310. Epub 2021 Jul 16.
Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model. Our CIL architecture is able to find optimal solutions, which we use to update the model with liver vessel CTA images. A novel ternary tree based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies. The tree topologies of the recipient's and donor's liver vessels are then used for an appropriate matching. The proposed algorithm utilizes a set of defined vessel tree variants which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL. We introduce a novel concept of in-order digital string based comparison to match the geometry of two anatomically varied trees. Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to state-of-the-art.
对供体-受体（LDR）对之间的肝血管解剖结构的直观理解可以帮助外科医生通过避免可能导致严重并发症的非靶向动脉来优化移植计划。我们建议通过使用级联增量学习（CIL）模型来直观地分析肝血管解剖结构的变体，以最大限度地提高寻找合适供体-受体（LDR）对的相似性。通过级联增量学习（CIL）模型对计算机断层血管造影（CTA）容积进行分割。我们的 CIL 架构能够找到最佳解决方案，我们使用这些解决方案通过肝血管 CTA 图像更新模型。提出了一种新的基于三进制树的算法，将所有可能的肝血管变体映射到各自的树拓扑结构中。然后，将受体和供体的肝血管的树拓扑结构用于进行适当的匹配。所提出的算法利用一组定义的血管树变体，通过利用 CIL 的增量学习能力得出的血管的精确分割结果，更新这些变体以维持最大的匹配选项。我们引入了一种基于有序数字串的比较新概念，以匹配两个解剖上不同的树的几何形状。通过可视化说明和定量分析的实验，证明了与最先进技术相比，我们的方法的有效性。
