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Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
[article]
2019
arXiv
pre-print
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The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss, which is supervised by segmentors, to reduce the geometric distortion. ...
We show that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. ...
Then we introduce our proposed medical volume-to-volume translation, with adversarial, cycle-consistency and shape-consistency losses, as well as dual-modality segmentation. ...
arXiv:1802.09655v2
fatcat:whzpssrdgvh7viogrbira6n3fu
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shapeconsistency loss, which is supervised by segmentors, to reduce the geometric distortion. ...
We show that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutuallybeneficial generators and segmentors for image synthesis and segmentation tasks. ...
Then we introduce our proposed medical volume-to-volume translation, with adversarial, cycle-consistency and shape-consistency losses, as well as dual-modality segmentation. ...
doi:10.1109/cvpr.2018.00963
dblp:conf/cvpr/ZhangYZ18
fatcat:bzu44wszjbdhhhgp6onb2wmkjq
Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration
[article]
2020
arXiv
pre-print
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The multimodal registration network can be effectively trained by computationally efficient similarity metrics without any ground-truth deformation. ...
Distinct from other translation-based methods that attempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodal problem (e.g., MR-to-MR) via image-to-image translation, our method leverages ...
The training loss of original Cycle-GAN only adopts two types of items: adversarial loss given by two discriminators (L D CT and L D M R ) and cycle-consistency loss L cyc to prevent generators from generating ...
arXiv:2007.02790v2
fatcat:wg2smzc7szdelpdd3spxmjgbia
Multimodal CT Image Synthesis Using Unsupervised Deep Generative Adversarial Networks for Stroke Lesion Segmentation
2022
Electronics
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In our approach, the CT samples generation and cross-modality translation differentiation are accomplished simultaneously by integrating a Siamesed auto-encoder architecture into the generative adversarial ...
In addition, a Gaussian mixture translation module is further proposed, which incorporates a translation loss to learn an intrinsic mapping between the latent space and the multimodal translation function ...
Adversarial and Cycle-Consistency Loss The adversarial and cycle-consistency loss from Cycle-GAN [30] are employed both in the generation cycle and discriminator. ...
doi:10.3390/electronics11162612
fatcat:gxymzf67uje5nfuse4anmcshpu
Review of Medical Image Synthesis using GAN Techniques
2021
ITM Web of Conferences
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Generative Adversarial Networks (GANs) is one of the vital efficient methods for generating a massive, high-quality artificial picture. ...
For diagnosing particular diseases in a medical image, a general problem is that it is expensive, usage of high radiation dosage, and time-consuming to collect data. ...
Adversarial training network regularize the texture and shape in the generator output which is important in the reconstruction and segmentation process of medical image processing. ...
doi:10.1051/itmconf/20213701005
fatcat:pd3vaaspendihh77aenbbfrqmy
DTR-GAN: An Unsupervised Bidirectional Translation Generative Adversarial Network for MRI-CT Registration
2023
Applied Sciences
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Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical ...
Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the ...
To alleviate the cycle-consistency loss limitation of the CycleGAN, which would lead to the generation of distorted shapes, we improve the CycleGAN with PatchNCE loss to generate a shape-consistent transformed ...
doi:10.3390/app14010095
fatcat:ptyi3pnzcfe3fogaesze7rljba
Medical Image Generation using Generative Adversarial Networks
[article]
2020
arXiv
pre-print
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The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical ...
image generation, image reconstruction, and image-to-image translation. ...
Generative Adversarial Network The Generative Adversarial Networks (GAN), introduced by Ian J. ...
arXiv:2005.10687v1
fatcat:5rg75wgww5d6vapjkfz4l2choi
GAN-based generation of realistic 3D data: A systematic review and taxonomy
[article]
2022
arXiv
pre-print
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Therefore, most of the publications on 3D Generative Adversarial Networks (GANs) are within the medical domain. ...
For example, in the medical field, rare diseases and privacy issues can lead to restricted data availability. ...
Acknowledgement This work received funding from enFaced (FWF KLI 678), enFaced 2.0 (FWF KLI 1044) and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative (https://kite.ikim.nrw/). ...
arXiv:2207.01390v1
fatcat:yny6btsy5zemjnbk7lnmxgsyzy
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
2019
IEEE Transactions on Medical Imaging
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images and a cycle-consistency loss for unregistered images. ...
Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. ...
Image Synthesis via Adversarial Networks Generative adversarial networks are neural-network architectures that consist of two sub-networks; G, a generator and D, a discriminator. ...
doi:10.1109/tmi.2019.2901750
pmid:30835216
fatcat:aebqpbsyebacpncspa64euty7u
TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical ...
The generator of TarGAN jointly learns mapping at two levels simultaneously - whole image translation mapping and target area translation mapping. ...
To address the above issues, we present a novel unified general-purpose multimodality medical image translation method named "Target-Aware Generative Adversarial Networks" (TarGAN). ...
arXiv:2105.08993v1
fatcat:nk6fuke4irbchpliqkmfylp36y
Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks
[article]
2019
arXiv
pre-print
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We utilize a cycle-consistent generative adversarial network to learn a bidirectional mapping between MRIs produced by GE Healthcare and Siemens scanners. ...
To ensure the preservation of breast shape and structures within the breast, we propose two technical innovations. ...
Multimodal translation and synthesis in medical imaging using CycleGAN should ensure shape consistency as anatomical structures are crucial in many computer-aided detection of cancer. ...
arXiv:1912.08061v1
fatcat:jeeeskb5qbagdal4ked2rytujq
Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
[article]
2022
arXiv
pre-print
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We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal ...
We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. ...
We thank Michele Volpi and his collaborators for kindly providing the Zurich Summer Dataset. ...
arXiv:2103.16262v2
fatcat:4prcahggnzevhexis4alwv5g4i
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.07044v2 fatcat:4uqyk7ud4fh4ni4yjixq2u7w4iOther Versions
This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images ...
domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. ...
Cycle generative adversarial network (CycleGAN) [30] has also been used for unsupervised medical image translation [18, 28, 12, 26] when paired images are not available. ...
arXiv:2105.07044v3
fatcat:nu7yijnei5h2dpwjwsnqmvv4h4
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
[article]
2019
arXiv
pre-print
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We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart ...
Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. ...
CONCLUSION We proposed an generative adversarial network called ANT-GAN for translating a medical image containing lesions into a corresponding image where the lesion has been "removed" via color correction ...
arXiv:1810.10850v2
fatcat:kz4ihpfirngxvm4vfs2qwl5kze
Multi-Domain Image Completion for Random Missing Input Data
[article]
2020
arXiv
pre-print
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Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding ...
completion and segmentation with a shared content encoder. ...
.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. ...
arXiv:2007.05534v1
fatcat:buih5jhb5javlla4mmz7v32eqm
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