ArticleBMC cancer2023
A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Comparison of surgical resection and radiofrequency ablation for the treatment of small hepatocellular carcinoma (≤ 3 cm): an updated meta-analysis.Systematic reviews · 2026Pooled it
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- Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
- Developing a fully automated imaging biomarker for HCC risk assessment via MRI-based tumor segmentation and EPM.Scientific reports · 2026Article
- Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.Radiology. Imaging cancer · 2026Article
- Liver Tumor Segmentation with Deep Learning: A Comparative Analysis of CNN-, Transformer-, and YOLO-Based Models on the ATLAS MRI.Diagnostics (Basel, Switzerland) · 2026Article
- An efficient pyramid scene parsing network with multi-scale feature fusion for liver segmentation in magnetic resonance imaging.Frontiers in medicine · 2026Article
- A ResNet-50-UNet Hybrid with Whale Optimization Algorithm for Accurate Liver Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study.JMIR medical informatics · 2025Article
- Liver Tumor Segmentation Based on Multi-Scale Deformable Feature Fusion and Global Context Awareness.Biomimetics (Basel, Switzerland) · 2025Article
- FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.Scientific reports · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Comparison of automated with manual 3D qEASL assessment based on MR imaging in hepatocellular carcinoma treated with conventional TACE.Abdominal radiology (New York) · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Immunotherapy in liver cancer: overcoming the tolerogenic liver microenvironment.Frontiers in immunology · 2024Review
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.Frontiers in oncology · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
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Authors and funding
6 authors at 4 institutions in 1 country.
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Abstract
objectiveRadiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.
methods105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time.
resultsThe average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively.
conclusionUNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.
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