125 research outputs found
INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping
We propose a fluid-based registration framework of medical images based on
implicit neural representation. By integrating implicit neural representation
and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a
Multilayer Perceptron (MLP) as a velocity generator while optimizing velocity
and image similarity. Moreover, we adopt a coarse-to-fine approach to address
the challenge of deformable-based registration methods dropping into local
optimal solutions, thus aiding the management of significant deformations in
medical image registration. Our algorithm has been validated on a paired
CT-CBCT dataset of 50 patients,taking the Dice coefficient of transferred
annotations as an evaluation metric. Compared to existing methods, our approach
achieves the state-of-the-art performance
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Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
During the process of computed tomography (CT), metallic implants often cause
disruptive artifacts in the reconstructed images, impeding accurate diagnosis.
Several supervised deep learning-based approaches have been proposed for
reducing metal artifacts (MAR). However, these methods heavily rely on training
with simulated data, as obtaining paired metal artifact CT and clean CT data in
clinical settings is challenging. This limitation can lead to decreased
performance when applying these methods in clinical practice. Existing
unsupervised MAR methods, whether based on learning or not, typically operate
within a single domain, either in the image domain or the sinogram domain. In
this paper, we propose an unsupervised MAR method based on the diffusion model,
a generative model with a high capacity to represent data distributions.
Specifically, we first train a diffusion model using CT images without metal
artifacts. Subsequently, we iteratively utilize the priors embedded within the
pre-trained diffusion model in both the sinogram and image domains to restore
the degraded portions caused by metal artifacts. This dual-domain processing
empowers our approach to outperform existing unsupervised MAR methods,
including another MAR method based on the diffusion model, which we have
qualitatively and quantitatively validated using synthetic datasets. Moreover,
our method demonstrates superior visual results compared to both supervised and
unsupervised methods on clinical datasets
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Denoising low-dose computed tomography (CT) images is a critical task in
medical image computing. Supervised deep learning-based approaches have made
significant advancements in this area in recent years. However, these methods
typically require pairs of low-dose and normal-dose CT images for training,
which are challenging to obtain in clinical settings. Existing unsupervised
deep learning-based methods often require training with a large number of
low-dose CT images or rely on specially designed data acquisition processes to
obtain training data. To address these limitations, we propose a novel
unsupervised method that only utilizes normal-dose CT images during training,
enabling zero-shot denoising of low-dose CT images. Our method leverages the
diffusion model, a powerful generative model. We begin by training a cascaded
unconditional diffusion model capable of generating high-quality normal-dose CT
images from low-resolution to high-resolution. The cascaded architecture makes
the training of high-resolution diffusion models more feasible. Subsequently,
we introduce low-dose CT images into the reverse process of the diffusion model
as likelihood, combined with the priors provided by the diffusion model and
iteratively solve multiple maximum a posteriori (MAP) problems to achieve
denoising. Additionally, we propose methods to adaptively adjust the
coefficients that balance the likelihood and prior in MAP estimations, allowing
for adaptation to different noise levels in low-dose CT images. We test our
method on low-dose CT datasets of different regions with varying dose levels.
The results demonstrate that our method outperforms the state-of-the-art
unsupervised method and surpasses several supervised deep learning-based
methods. Codes are available in https://github.com/DeepXuan/Dn-Dp
Three-Dimensional Medical Image Fusion with Deformable Cross-Attention
Multimodal medical image fusion plays an instrumental role in several areas
of medical image processing, particularly in disease recognition and tumor
detection. Traditional fusion methods tend to process each modality
independently before combining the features and reconstructing the fusion
image. However, this approach often neglects the fundamental commonalities and
disparities between multimodal information. Furthermore, the prevailing
methodologies are largely confined to fusing two-dimensional (2D) medical image
slices, leading to a lack of contextual supervision in the fusion images and
subsequently, a decreased information yield for physicians relative to
three-dimensional (3D) images. In this study, we introduce an innovative
unsupervised feature mutual learning fusion network designed to rectify these
limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB)
module that facilitates the dual modalities in discerning their respective
similarities and differences. We have applied our model to the fusion of 3D MRI
and PET images obtained from 660 patients in the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB
module, our network generates high-quality MRI-PET fusion images. Experimental
results demonstrate that our method surpasses traditional 2D image fusion
methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and
Structural Similarity Index Measure (SSIM). Importantly, the capacity of our
method to fuse 3D images enhances the information available to physicians and
researchers, thus marking a significant step forward in the field. The code
will soon be available online
A Matlab Toolbox for Feature Importance Ranking
More attention is being paid for feature importance ranking (FIR), in
particular when thousands of features can be extracted for intelligent
diagnosis and personalized medicine. A large number of FIR approaches have been
proposed, while few are integrated for comparison and real-life applications.
In this study, a matlab toolbox is presented and a total of 30 algorithms are
collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound
images. To each breast mass lesion, 15 features are extracted. To figure out
the optimal subset of features for classification, all combinations of features
are tested and linear support vector machine is used for the malignancy
prediction of lesions annotated in ultrasound images. At last, the
effectiveness of FIR is analyzed according to performance comparison. The
toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work,
more FIR methods, feature selection methods and machine learning classifiers
will be integrated
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