ArticleMagnetic resonance in medicine2019
SUSAN: segment unannotated image structure using adversarial network.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Enhanced Magnetic Resonance Imaging-Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study.JMIR medical informatics · 2026Article
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- A fully automatic knee subregion segmentation network based on tissue segmentation and anatomical geometry.Scientific reports · 2025Article
- A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.Quantitative imaging in medicine and surgery · 2024Article
- DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.Quantitative imaging in medicine and surgery · 2023Article
- 3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs.IEEE journal of biomedical and health informatics · 2023Article
- [A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.Healthcare (Basel, Switzerland) · 2022Article
- Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning.Quantitative imaging in medicine and surgery · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
- Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?Skeletal radiology · 2022Review
- Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.Nature reviews. Rheumatology · 2022Review
- Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI.Diagnostics (Basel, Switzerland) · 2022Article
- Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.Journal of healthcare engineering · 2022Review
- Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.Magma (New York, N.Y.) · 2021Article
- MR-contrast-aware image-to-image translations with generative adversarial networks.International journal of computer assisted radiology and surgery · 2021Article
- Improved amyloid burden quantification with nonspecific estimates using deep learning.European journal of nuclear medicine and molecular imaging · 2021Article
- Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging.Radiology. Artificial intelligence · 2021Review
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Abstract
purposeTo describe and evaluate a segmentation method using joint adversarial and segmentation convolutional neural network to achieve accurate segmentation using unannotated MR image datasets. THEORY AND
methodsA segmentation pipeline was built using joint adversarial and segmentation network. A convolutional neural network technique called cycle-consistent generative adversarial network (CycleGAN) was applied as the core of the method to perform unpaired image-to-image translation between different MR image datasets. A joint segmentation network was incorporated into the adversarial network to obtain additional functionality for semantic segmentation. The fully automated segmentation method termed as SUSAN was tested for segmenting bone and cartilage on 2 clinical knee MR image datasets using images and annotated segmentation masks from an online publicly available knee MR image dataset. The segmentation results were compared using quantitative segmentation metrics with the results from a supervised U-Net segmentation method and 2 registration methods. The Wilcoxon signed-rank test was used to evaluate the value difference of quantitative metrics between different methods.
resultsThe proposed method SUSAN provided high segmentation accuracy with results comparable to the supervised U-Net segmentation method (most quantitative metrics having P > 0.05) and significantly better than a multiatlas registration method (all quantitative metrics having P < 0.001) and a direct registration method (all quantitative metrics having P< 0.0001) for the clinical knee image datasets. SUSAN also demonstrated the applicability for segmenting knee MR images with different tissue contrasts.
conclusionSUSAN performed rapid and accurate tissue segmentation for multiple MR image datasets without the need for sequence specific segmentation annotation. The joint adversarial and segmentation network and training strategy have promising potential applications in medical image segmentation.
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