10,428 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
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
In recent years, endomicroscopy has become increasingly used for diagnostic
purposes and interventional guidance. It can provide intraoperative aids for
real-time tissue characterization and can help to perform visual investigations
aimed for example to discover epithelial cancers. Due to physical constraints
on the acquisition process, endomicroscopy images, still today have a low
number of informative pixels which hampers their quality. Post-processing
techniques, such as Super-Resolution (SR), are a potential solution to increase
the quality of these images. SR techniques are often supervised, requiring
aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
train a model. However, in our domain, the lack of HR images hinders the
collection of such pairs and makes supervised training unsuitable. For this
reason, we propose an unsupervised SR framework based on an adversarial deep
neural network with a physically-inspired cycle consistency, designed to impose
some acquisition properties on the super-resolved images. Our framework can
exploit HR images, regardless of the domain where they are coming from, to
transfer the quality of the HR images to the initial LR images. This property
can be particularly useful in all situations where pairs of LR/HR are not
available during the training. Our quantitative analysis, validated using a
database of 238 endomicroscopy video sequences from 143 patients, shows the
ability of the pipeline to produce convincing super-resolved images. A Mean
Opinion Score (MOS) study also confirms this quantitative image quality
assessment.Comment: Accepted for publication on Medical Image Analysis journa
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