ArticleAbdominal radiology (New York)2021
Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A knowledge-guided dual-path framework for automated liver MRI series classification.Abdominal radiology (New York) · 2026Article
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Fine-grained Prototype Network for MRI Sequence Classification.Current medical imaging · 2025Article
- [Application and prospects of magnetic resonance imaging techniques in the diagnosis and evaluation of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024Review
- SenseCare: a research platform for medical image informatics and interactive 3D visualization.Frontiers in radiology · 2024Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
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10 authors.
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Abstract
purposeTo develop and validate a dense feature fusion neural network (DFuNN) to automatically recognize different sequences and phases of liver magnetic resonance imaging (MRI). MATERIALS AND
methodsIn total, 3869 sequences and phases from 384 liver MRI examinations, divided into training/validation (n = 2886 sequences from 287 patients) and test (n = 983 sequences from 97 patients) sets, were used in this retrospective study. Ten unenhanced sequences and enhanced phases were included. Manual sequence recognition, performed by two radiologists (20 and 10 years of experience) in a consensus reading, was used as the reference standard. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the DFuNN on an identical unseen test set. Finally, we evaluated the factors impacting the model precision.
resultsA fusion block improved the performance of the DFuNN. DFuNN with a fusion block achieved good recognition performance for both complete and incomplete sequences and phases in the test set. The average sensitivity of recognition performance for complete sequence and phase inputs ranged from 88.06 to 100%, the average specificity ranged from 99.12 to 99.94%, and the median accuracy ranged from 98.02 to 99.95%. The DFuNN prediction accuracy for patients without cirrhosis were significantly higher than those for patients with cirrhosis (P = 0.0153). No significant difference was found in the accuracy across other factors.
conclusionDFuNN can automatically and accurately identify specific unenhanced MRI sequences and enhanced MRI phases.
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