ArticleBMC medical imaging2022
Practical utility of liver segmentation methods in clinical surgeries and interventions.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 53 papers, 4 of them syntheses that pooled it.
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
53 citing papers in PubMed, 4 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review.Cancer medicine · 2023Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.Biomedicines · 2026Article
- Computer-assisted Treatment Planning and Mathematical Modeling for Percutaneous Liver Tumor Ablation: An Updated Survey.Annals of biomedical engineering · 2026Review
- Automated deep learning for real-time focal liver lesions detection in ultrasound videos a multicenter study.NPJ digital medicine · 2026Article
- Longitudinal deep learning models for tracking disease progression in ovarian cancer using PET/CT imaging and clinical reports.Physical and engineering sciences in medicine · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification.Physical and engineering sciences in medicine · 2025Article
- Treatment of recurrent hepatocellular carcinoma: The current standards and future perspectives.World journal of gastrointestinal oncology · 2025Review
- Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study.JMIR medical informatics · 2025Article
- Reliability of Whole-Liver Liver-Fat-Quantification Between Deep Learning-Accelerated and Standard Volumetric Interpolated Breath-hold Examination Dixon Sequences in a Prospective Oncology Cohort.Investigative radiology · 2025Article
- Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.Scientific reports · 2025Article
- FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.Scientific reports · 2025Article
- Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.Scientific reports · 2025Article
- Automatic computed tomography image segmentation method for liver tumor based on a modified tokenized multilayer perceptron and attention mechanism.Quantitative imaging in medicine and surgery · 2025Article
- Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Case Report: Hepatocellular adenoma due to long-term oral stanozolol administration.Frontiers in medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012-2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.
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.