10,007 research outputs found
Conditional Diffusion Models for Semantic 3D Medical Image Synthesis
This paper introduces Med-DDPM, an innovative solution using diffusion models
for semantic 3D medical image synthesis, addressing the prevalent issues in
medical imaging such as data scarcity, inconsistent acquisition methods, and
privacy concerns. Experimental evidence illustrates that diffusion models
surpass Generative Adversarial Networks (GANs) in stability and performance,
generating high-quality, realistic 3D medical images. The distinct feature of
Med-DDPM is its use of semantic conditioning for the diffusion model in 3D
image synthesis. By controlling the generation process through pixel-level mask
labels, it facilitates the creation of realistic medical images. Empirical
evaluations underscore the superior performance of Med-DDPM over GAN techniques
in metrics such as accuracy, stability, and versatility. Furthermore, Med-DDPM
outperforms traditional augmentation techniques and synthetic GAN images in
enhancing the accuracy of segmentation models. It addresses challenges such as
insufficient datasets, lack of annotated data, and class imbalance. Noting the
limitations of the Frechet inception distance (FID) metric, we introduce a
histogram-equalized FID metric for effective performance evaluation. In
summary, Med-DDPM, by utilizing diffusion models, signifies a crucial step
forward in the domain of high-resolution semantic 3D medical image synthesis,
transcending the limitations of GANs and data constraints. This method paves
the way for a promising solution in medical imaging, primarily for data
augmentation and anonymization, thus contributing significantly to the field
Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks
Sonography synthesis has a wide range of applications, including medical
procedure simulation, clinical training and multimodality image registration.
In this paper, we propose a machine learning approach to simulate ultrasound
images at given 3D spatial locations (relative to the patient anatomy), based
on conditional generative adversarial networks (GANs). In particular, we
introduce a novel neural network architecture that can sample anatomically
accurate images conditionally on spatial position of the (real or mock)
freehand ultrasound probe. To ensure an effective and efficient spatial
information assimilation, the proposed spatially-conditioned GANs take
calibrated pixel coordinates in global physical space as conditioning input,
and utilise residual network units and shortcuts of conditioning data in the
GANs' discriminator and generator, respectively. Using optically tracked B-mode
ultrasound images, acquired by an experienced sonographer on a fetus phantom,
we demonstrate the feasibility of the proposed method by two sets of
quantitative results: distances were calculated between corresponding
anatomical landmarks identified in the held-out ultrasound images and the
simulated data at the same locations unseen to the networks; a usability study
was carried out to distinguish the simulated data from the real images. In
summary, we present what we believe are state-of-the-art visually realistic
ultrasound images, simulated by the proposed GAN architecture that is stable to
train and capable of generating plausibly diverse image samples.Comment: Accepted to MICCAI RAMBO 201
Mask-conditioned latent diffusion for generating gastrointestinal polyp images
In order to take advantage of AI solutions in endoscopy diagnostics, we must
overcome the issue of limited annotations. These limitations are caused by the
high privacy concerns in the medical field and the requirement of getting aid
from experts for the time-consuming and costly medical data annotation process.
In computer vision, image synthesis has made a significant contribution in
recent years as a result of the progress of generative adversarial networks
(GANs) and diffusion probabilistic models (DPM). Novel DPMs have outperformed
GANs in text, image, and video generation tasks. Therefore, this study proposes
a conditional DPM framework to generate synthetic GI polyp images conditioned
on given generated segmentation masks. Our experimental results show that our
system can generate an unlimited number of high-fidelity synthetic polyp images
with the corresponding ground truth masks of polyps. To test the usefulness of
the generated data, we trained binary image segmentation models to study the
effect of using synthetic data. Results show that the best micro-imagewise IOU
of 0.7751 was achieved from DeepLabv3+ when the training data consists of both
real data and synthetic data. However, the results reflect that achieving good
segmentation performance with synthetic data heavily depends on model
architectures
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
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