ArticleIEEE transactions on medical imaging2020
One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.Journal of neuroscience methods · 2025Article
- Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.Magnetic resonance in medicine · 2025Article
- Attentional adversarial training for few-shot medical image segmentation without annotations.PloS one · 2024Article
- Adversarial-based latent space alignment network for left atrial appendage segmentation in transesophageal echocardiography images.Frontiers in cardiovascular medicine · 2023Article
- Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation.IEEE transactions on medical imaging · 2022Article
- Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network.PloS one · 2022Article
- Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.IEEE transactions on medical imaging · 2021Article
- ABCnet: Adversarial bias correction network for infant brain MR images.Medical image analysis · 2021Article
- Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring.Diagnostics (Basel, Switzerland) · 2021Article
- Multi-Domain Image Completion for Random Missing Input Data.IEEE transactions on medical imaging · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.
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