ArticleAn international journal on information fusion2021
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis.
Article in An international journal on information fusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative adversarial networks in dental imaging: a systematic review.Oral radiology · 2024Pooled it
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- ReeGAN: MRI image edge-preserving synthesis based on GANs trained with misaligned data.Medical & biological engineering & computing · 2024Article
- MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.BMC medical imaging · 2024Article
- The quest for early detection of retinal disease: 3D CycleGAN-based translation of optical coherence tomography into confocal microscopy.Biological imaging · 2024Article
- Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images.Physics in medicine and biology · 2023Article
- Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.Medical image analysis · 2022Article
- Generative adversarial networks and synthetic patient data: current challenges and future perspectives.Future healthcare journal · 2022Article
- New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning.iScience · 2022Article
- A review of deep learning-based deformable medical image registration.Frontiers in oncology · 2022Review
- CryoETGAN: Cryo-Electron Tomography Image SynthesisFrontiers in physiology · 2022Article
- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop) · 2021Article
- Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.European journal of nuclear medicine and molecular imaging · 2021Article
- Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification.Frontiers in oncology · 2021Article
- Diffusion model-based contrast-enhanced CT synthesis for breast cancer radiotherapy: Pursuing contrast-free imaging.Science progressArticle
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8 authors.
Funding
Abstract
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.