MRI to CT Translation with GANs
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we describe and implement three state of the art convolutional neural network (CNN) and generative adversarial network (GAN) models where we report statistics and visual results of two of them.
Results and benchmarks
We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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- Start from related paper: Review of Medical Image Synthesis using GAN Techniques.
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Time to first repro: a few days
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Reproduction readiness
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Hardware requirements
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Validation caveat
Hugging Face artifacts
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Models
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Research context
7
Citations
20
References
Tasks
Convolutional neural network, Translation (biology), Preprocessor, Computer science, Generative adversarial network, Magnetic resonance imaging, Computed tomography, Task (project management)
Methods
None detected
Domains
Artificial intelligence, Image (mathematics)
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