ArticleBMC bioinformatics2019
Liver tumor segmentation in CT volumes using an adversarial densely connected network.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- SLIC-Former: a superpixel-guided transformer framework for automatic liver segmentation in CT images.Scientific reports · 2026Article
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- From CNNs to SAM: A Survey of Deep Learning Techniques for Liver Tumor Segmentation in CT Images.IEEE access : practical innovations, open solutions · 2025Article
- A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.BMC cancer · 2023Article
- Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.World journal of gastroenterology · 2023Review
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- 3D reconstruction of proximal femoral fracture from biplanar radiographs with fractural representative learning.Scientific reports · 2023Article
- Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks.Annals of translational medicine · 2021Article
- Design of lung nodules segmentation and recognition algorithm based on deep learning.BMC bioinformatics · 2021Article
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7 authors.
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Abstract
backgroundMalignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task.
resultsWe perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method.
conclusionsThe testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.
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