3D Brain and Heart Volume Generative Models: A Survey

Liu, Yanbin; Dwivedi, Girish; Boussaid, Farid; Bennamoun, Mohammed

doi:10.48550/arxiv.2210.05952

Cited by 2 publications

(2 citation statements)

References 157 publications

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“…In total this results in 139221, 6891, and 27956 2d slices for training, validation, and testing, respectively. Based on the survey papers [15,23,1], we selected to most used metrics for evaluating our models: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and multi-scale structural similarity index (MS-SSIM). The SSIM is a measure of the similarity between two images.…”

Section: Experiments and Resultsmentioning

confidence: 99%

fRegGAN with K-space Loss Regularization for Medical Image Translation

Baltruschat¹,

Kreis²,

Hoelscher³

et al. 2023

Preprint

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Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation tasks. However, GANs tend to suffer from a frequency bias towards low frequencies, which can lead to the removal of important structures in the generated images. To address this issue, we propose a novel frequency-aware image-to-image translation framework based on the supervised RegGAN approach, which we call fRegGAN. The framework employs a K-space loss to regularize the frequency content of the generated images and incorporates wellknown properties of MRI K-space geometry to guide the network training process. By combine our method with the RegGAN approach, we can mitigate the effect of training with misaligned data and frequency bias at the same time. We evaluate our method on the public BraTS dataset and outperform the baseline methods in terms of both quantitative and qualitative metrics when synthesizing T2-weighted from T1-weighted MR images. Detailed ablation studies are provided to understand the effect of each modification on the final performance. The proposed method is a step towards improving the performance of image-to-image translation and synthesis in the medical domain and shows promise for other applications in the field of image processing and generation.

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Section: Experiments and Resultsmentioning

confidence: 99%

fRegGAN with K-space Loss Regularization for Medical Image Translation

Baltruschat¹,

Kreis²,

Hoelscher³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Concretely, the complexity of data collection procedures, the lack of experts, privacy concerns, and the compulsory requirement of authorization from patients create a major bottleneck in the annotation process in medical imaging. This is where generative models become advantageous [68]. Several perspectives have driven our interest in generative diffusion models for medical imaging.…”

Section: Clinical Importancementioning

confidence: 99%

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Kazerouni¹,

Aghdam²,

Heidari³

et al. 2022

Preprint

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Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including segmentation, anomaly detection, image-to-image translation, 2/3D generation, reconstruction, denoising, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub 1 . We aim to update the relevant latest papers within it regularly.

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3D Brain and Heart Volume Generative Models: A Survey

Cited by 2 publications

References 157 publications

fRegGAN with K-space Loss Regularization for Medical Image Translation

fRegGAN with K-space Loss Regularization for Medical Image Translation

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

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