ArticleScientific reports2022
A lightweight neural network with multiscale feature enhancement for liver CT segmentation.
Article in Scientific reports, 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 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Deep learning model based on CT images to predict Ki-67 expression in renal carcinoma.Oncology letters · 2026Article
- A preclinical CT and MRI Liver Imaging Dataset with Anatomical, Functional and Segmentation Data.Scientific data · 2026Article
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- Text-guided few-shot liver and tumor segmentation.Frontiers in digital health · 2026Article
- Deep learning models for segmentation and quantification of left atrial appendage volume using noncontrast cardiac computed tomography.Journal of cardiovascular imaging · 2025Article
- FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.Journal of cancer research and clinical oncology · 2025Article
- Placenta segmentation redefined: review of deep learning integration of magnetic resonance imaging and ultrasound imaging.Visual computing for industry, biomedicine, and art · 2025Review
- PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation.Visual computing for industry, biomedicine, and art · 2025Article
- Dual-channel compression mapping network with fused attention mechanism for medical image segmentation.Scientific reports · 2025Article
- Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.Scientific reports · 2025Article
- UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.BMC medical imaging · 2025Article
- Application value of CT three-dimensional reconstruction technology in the identification of benign and malignant lung nodules and the characteristics of nodule distribution.BMC medical imaging · 2025Article
- Evaluating the three-level approach of the U-smile method for imbalanced binary classification.PloS one · 2025Article
- Pilot study of an arterial enhancement fraction-based model for progression prediction in HCC undergoing chemoembolization.Frontiers in oncology · 2025Article
- Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images.BMC medical imaging · 2024Article
- Myocardial strain analysis by cardiac magnetic resonance associated with arrhythmias in repaired tetralogy of Fallot patients.BMC medical imaging · 2024Article
- Contrast-enhanced ultrasonography guidance avoids US-CT/MR fusion error for percutaneous radiofrequency ablation of hepatocellular carcinoma.BMC medical imaging · 2024Article
- Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images.BMC medical imaging · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.
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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.