ArticleScientific reports2022
Deep 3D attention CLSTM U-Net based automated liver segmentation and volumetry for the liver transplantation in abdominal CT volumes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Review
- Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.Current medical imaging · 2025Article
- Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.BMC medical imaging · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- A Review of Advancements and Challenges in Liver Segmentation.Journal of imaging · 2024Review
- Multivariable linear model for predicting graft weight based on 3-dimensional volumetry in regards to body weight change of living liver donor: an observational cohort study.Annals of surgical treatment and research · 2024Article
- Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Correlation between the liver transection line localization and future liver remnant hypertrophy in associating liver partition and portal vein ligation for staged hepatectomy.Frontiers in surgery · 2024Article
- Article
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- Article
- Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional Convolutional Long Short-Term Memory Architecture.Sensors (Basel, Switzerland) · 2022Article
- Fully automated preoperative liver volumetry incorporating the anatomical location of the central hepatic vein.Scientific reports · 2022Article
- A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams.Scientific reports · 2022Article
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Authors and funding
5 authors.
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Abstract
In living-donor liver transplantation, the safety of the donor is critical. In addition, accurately measuring the liver volume is significant as the amount that can be resected from living donors is limited. In this paper, we propose an automated segmentation and volume estimation method for the liver in computed tomography imaging based on a deep learning-based segmentation network. Our framework was trained using the data of 191 donors, achieved a dice similarity coefficient of 0.789, 0.869, 0.955, and 0.899, respectively, in the segmentation task for the left lobe, right lobe, caudate lobe, and whole liver. Moreover, the R^2 score reached 0.980, 0.996, 0.953, and 0.996 in the volume estimation task. We demonstrate that our approach provides accurate and quantitative liver segmentation results, reducing the error in liver volume estimation. Therefore, we expected to be used as an aid in estimating liver volume from CT volume data for living-donor liver transplantation.
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