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http://hdl.handle.net/10397/105482
Title: | Living donor-recipient pair matching for liver transplant via ternary tree representation with cascade incremental learning | Authors: | Nazir, A Cheema, MN Sheng, B Li, P Kim, J Lee, TY |
Issue Date: | Aug-2021 | Source: | IEEE transactions on biomedical engineering, Aug. 2021, v. 68, no. 8, p. 2540-2551 | Abstract: | 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. | Keywords: | Computed tomography angiography Image enhancement Incremental learning Liver transplantation Liver variants Ternary tree representation Vessels segmentation |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on biomedical engineering | ISSN: | 0018-9294 | EISSN: | 1558-2531 | DOI: | 10.1109/TBME.2021.3050310 | Rights: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication A. Nazir, M. N. Cheema, B. Sheng, P. Li, J. Kim and T. -Y. Lee, "Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2540-2551, Aug. 2021 is available at https://doi.org/10.1109/TBME.2021.3050310. |
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Li_Living_Donor-Recipient_Pair.pdf | Pre-Published version | 14.11 MB | Adobe PDF | View/Open |
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