ArticleBMC medical informatics and decision making2021
U-Net combined with multi-scale attention mechanism for liver segmentation in CT images.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion.Bioengineering (Basel, Switzerland) · 2025Article
- Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.Journal of medical radiation sciences · 2024Article
- Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images.Bioengineering (Basel, Switzerland) · 2024Article
- Deep-learning-based pelvic automatic segmentation in pelvic fractures.Scientific reports · 2024Article
- Article
- Improving a Deep Learning Model to Accurately Diagnose LVNC.Journal of clinical medicine · 2023Article
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation.Computational intelligence and neuroscience · 2022Article
- MLAU-Net: Deep supervised attention and hybrid loss strategies for enhanced segmentation of low-resolution kidney ultrasound.Digital healthArticle
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9 authors.
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Abstract
backgroundThe liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).
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