RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
Open accessAbstract
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
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- Authors
- Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Tianjin University of Traditional Chinese Medicine (CN)
- La Trobe University (AU)
- Tianjin University (CN)
- Corresponding author
- Su, Ran
- Venue
- Frontiers in Bioengineering and Biotechnology
- Publisher
- Frontiers Media SA
- Published
- 2020-12-23
- Volume
- 8
- Pages
- 605132-605132
- ISSN
- 2296-4185 (ISSN-L)
- PMC
- PMC7785874
- OpenAlex
- W2899279931
- Cited by
- 468
- References
- 70
- Citations by year
- 2026: 32 · 2025: 78 · 2024: 81 · 2023: 102 · 2022: 92 · 2021: 53 · 2020: 23 · 2019: 7
- FWCI
- 26.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Bioengineering and Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (16ZXHLGX00170, 61702361)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- liver tumor segmentation, liver segmentation, 3D medical image segmentation, attention mechanism, residual attention module, U-Net, computed tomography, volumetric medical images, 3DIRCADb
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