ReviewSensors (Basel, Switzerland)2021
Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- A study of cross-dataset generalization on liver vessel segmentation improved by a topological loss.Medical & biological engineering & computing · 2026Article
- Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Fusing photoacoustic microscopy and ultrasound imaging: an artificial intelligence strategy for vessel enhancement in interventional guidance.Biomedical optics express · 2026Article
- Automated segmentation of hepatic vessels and lobules in whole-slide images using U-net models.Frontiers in bioinformatics · 2026Article
- MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.European radiology experimental · 2025Article
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.Journal of clinical medicine · 2025Article
- TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images.Diagnostics (Basel, Switzerland) · 2025Article
- Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced residual blocks for medical image segmentation.Quantitative imaging in medicine and surgery · 2024Article
- Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.The Journal of international medical research · 2024Review
- Automatic vessel attenuation measurement for quality control of contrast-enhanced CT: Validation on the portal vein.Medical physics · 2024Article
- Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.Scientific reports · 2024Article
- Suitability of DNN-based vessel segmentation for SIRT planning.International journal of computer assisted radiology and surgery · 2024Article
- Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion.International journal of computer assisted radiology and surgery · 2024Article
- Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study.Biomedical physics & engineering express · 2023Article
- Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention.BMC medical imaging · 2023Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net.Diagnostics (Basel, Switzerland) · 2023Article
- A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation.International journal of computer assisted radiology and surgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.