1,730 research outputs found
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.Comment: 16 pages, 8 figure
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive
microstructure assessment technique. Scalar measures, such as FA (fractional
anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue
properties can be obtained using diffusion models and data processing
pipelines. However, it is costly and time consuming to collect high quality
diffusion data. Here, we therefore demonstrate how Generative Adversarial
Networks (GANs) can be used to generate synthetic diffusion scalar measures
from structural T1-weighted images in a single optimized step. Specifically, we
train the popular CycleGAN model to learn to map a T1 image to FA or MD, and
vice versa. As an application, we show that synthetic FA images can be used as
a target for non-linear registration, to correct for geometric distortions
common in diffusion MRI
Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images. For
example, metallic implants will lead to localized perturbations in MRI scans.
This will affect further post-processing tasks such as attenuation correction
in PET/MRI or radiation therapy planning. In this work, we propose the
inpainting of medical images via Generative Adversarial Networks (GANs). The
proposed framework incorporates two patch-based discriminator networks with
additional style and perceptual losses for the inpainting of missing
information in realistically detailed and contextually consistent manner. The
proposed framework outperformed other natural image inpainting techniques both
qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201
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