Living donor-recipient pair matching for liver transplant via ternary tree representation with cascade incremental learning

Anam Nazir, Muhammad Nadeem Cheema, Bin Sheng, Ping Li, Jinman Kim, Tong Yee Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number9317846
Pages (from-to)2540-2551
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number8
Publication statusPublished - Aug 2021


  • Image enhancement
  • computed tomography angiography
  • incremental learning
  • liver transplantation
  • liver variants
  • ternary tree representation
  • vessels segmentation

ASJC Scopus subject areas

  • Biomedical Engineering


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