Ternary Tree Representation

Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation with Cascade Incremental Learning

Author(s): Anam Nazir, Muhammad Nadeem Cheema, Bin Sheng, Ping Li, Jinman Kim, Tong-Yee Lee
Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation with Cascade Incremental Learning 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This work visually analyzes anatomical variants of liver vessels anatomy to maximize similarity for finding suitable living donor-recipient pairs. We leverage incremental learning in a cascade feature mapping way via updating input CTA training model to optimize segmentation capability. A ternary-tree-based approach is proposed to map all possible liver vessel variants into their respective tree topologies. The ternary tree in-order traversing is designed to efficiently compare the digital strings of two anatomically varied vessel structures to find a suitable match. Experiments through visual illustrations and quantitative analysis demonstrated our method computed very efficiently for finer visualization of liver tree structures. read more