ReviewInsights into imaging2017
Liver segmentation: indications, techniques and future directions.
Review in Insights into imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 213 citations in OpenAlex.
- Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.Abdominal radiology (New York) · 2024Pooled it
- Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review.Frontiers in physiology · 2021Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2019Trial
- Comparing the reliability of three software programmes for manual and (semi-)automatic volumetry in healthy livers.World journal of radiology · 2026Article
- Additive value of oncological resectability criteria for predicting post-hepatectomy liver failure in hepatocellular carcinoma.Updates in surgery · 2026Article
- A Real-World Evaluation of Failure Detection for Liver CT Segmentation.medRxiv : the preprint server for health sciences · 2026Article
- Liver volumetry software: a narrative review of technical performance and clinical utility.Surgical endoscopy · 2026Review
- Seeing the invisible: Three-dimensional liver modeling from simulation to the scalpel.Annals of hepato-biliary-pancreatic surgery · 2026Review
- A Multi-center Gadolinium-ethoxybenzyl-diethylenetriamine Pentaacetic Acid (Gd-EOB-DTPA) MRI Dataset with Expert Annotations and clinicopathological data.Scientific data · 2026Article
- A Systematic Review of Deep Learning Approaches for Hepatopancreatic Tumor Segmentation.Journal of imaging · 2026Review
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Radiology for surgeons: liver anatomy.Frontiers in surgery · 2026Review
- CRLM-GAN: a feature-constrained GAN-based deep learning framework for multi-parametric MRI-based segmentation of colorectal liver metastases before and after chemotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.Current medical imaging · 2025Article
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Explainable and Robust Deep Learning for Liver Segmentation Through U-Net Network.Diagnostics (Basel, Switzerland) · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLiver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals.
methodsUsing images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice.
resultsLiver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the "gold standard" in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers.
conclusionsLiver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice. TEACHING POINTS: • Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers.
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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.