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

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
期刊IEEE Transactions on Biomedical Engineering
DOIs
出版狀態Accepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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