IEE Transactions on Medical Imaging This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction Using Mesh Priors
Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of cardiovascular events and determine the treatment. Clinical analysis of coronary arteries in CCTA comprises the identification of atherosclerotic plaque, as well as the grading of any coronary artery stenosis typically obtained through the CAD-Reporting and Data System (CAD-RADS). This requires analysis of the coronary lumen and plaque. While voxel-wise segmentation is a commonly used approach in various segmentation tasks, it does not guarantee topologically...
Source: IEE Transactions on Medical Imaging - April 5, 2024 Category: Biomedical Engineering Source Type: research

Table of Contents
null (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 3, 2024 Category: Biomedical Engineering Source Type: research

Distinctive Phase Interdependency Model for Retinal Vasculature Delineation in OCT-Angiography Images
Automatic detection of retinal vasculature in optical coherence tomography angiography (OCTA) images faces several challenges such as the closely located capillaries, vessel discontinuity and high noise level. This paper introduces a new distinctive phase interdependency model to address these problems for delineating centerline patterns of the vascular network. We capture the inherent property of vascular centerlines by obtaining the inter-scale dependency information that exists between neighboring symmetrical wavelets in complex Poisson domain. In particular, the proposed phase interdependency model identifies vascular ...
Source: IEE Transactions on Medical Imaging - March 8, 2024 Category: Biomedical Engineering Source Type: research

Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image Segmentation Under Limited Computational Resources
Hybrid transformer-based segmentation approaches have shown great promise in medical image analysis. However, they typically require considerable computational power and resources during both training and inference stages, posing a challenge for resource-limited medical applications common in the field. To address this issue, we present an innovative framework called Slim UNETR, designed to achieve a balance between accuracy and efficiency by leveraging the advantages of both convolutional neural networks and transformers. Our method features the Slim UNETR Block as a core component, which effectively enables information e...
Source: IEE Transactions on Medical Imaging - March 8, 2024 Category: Biomedical Engineering Source Type: research

Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect dia...
Source: IEE Transactions on Medical Imaging - March 8, 2024 Category: Biomedical Engineering Source Type: research

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations to rare diseases. In this paper, we propose FoPro-KD, a novel framework that leverages the power of frequency patterns learned from frozen pre-trained models to enhance their transferability and compression, presenting a few unique insights: 1) We demonstrate that leve...
Source: IEE Transactions on Medical Imaging - March 8, 2024 Category: Biomedical Engineering Source Type: research

Table of Contents
null (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - March 5, 2024 Category: Biomedical Engineering Source Type: research

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applicat...
Source: IEE Transactions on Medical Imaging - February 6, 2024 Category: Biomedical Engineering Source Type: research

A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering
In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQ...
Source: IEE Transactions on Medical Imaging - February 6, 2024 Category: Biomedical Engineering Source Type: research

Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention
Image segmentation is essential to medical image analysis as it provides the labeled regions of interest for the subsequent diagnosis and treatment. However, fully-supervised segmentation methods require high-quality annotations produced by experts, which is laborious and expensive. In addition, when performing segmentation on another unlabeled image modality, the segmentation performance will be adversely affected due to the domain shift. Unsupervised domain adaptation (UDA) is an effective way to tackle these problems, but the performance of the existing methods is still desired to improve. Also, despite the effectivenes...
Source: IEE Transactions on Medical Imaging - February 6, 2024 Category: Biomedical Engineering Source Type: research

Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model
The superiority of magnetic resonance (MR)-only radiotherapy treatment planning (RTP) has been well demonstrated, benefiting from the synthesis of computed tomography (CT) images which supplements electron density and eliminates the errors of multi-modal images registration. An increasing number of methods has been proposed for MR-to-CT synthesis. However, synthesizing CT images of different anatomical regions from MR images with different sequences using a single model is challenging due to the large differences between these regions and the limitations of convolutional neural networks in capturing global context informat...
Source: IEE Transactions on Medical Imaging - February 6, 2024 Category: Biomedical Engineering Source Type: research

Robust Deformable Image Registration Using Cycle-Consistent Implicit Representations
Recent works in medical image registration have proposed the use of Implicit Neural Representations, demonstrating performance that rivals state-of-the-art learning-based methods. However, these implicit representations need to be optimized for each new image pair, which is a stochastic process that may fail to converge to a global minimum. To improve robustness, we propose a deformable registration method using pairs of cycle-consistent Implicit Neural Representations: each implicit representation is linked to a second implicit representation that estimates the opposite transformation, causing each network to act as a reg...
Source: IEE Transactions on Medical Imaging - February 6, 2024 Category: Biomedical Engineering Source Type: research

Correction to “A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation”
In the above article [1], there are errors on pages 2045 and 2046. Section METHOD.D and Section METHOD.E should be the subsections of Section METHOD.C, i.e., METHOD.C: 1) Relation Graph Construction; 2) Message Passing via Relation Graph; and 3) Mapping Disease Relation to Regions. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - February 2, 2024 Category: Biomedical Engineering Source Type: research

Table of Contents
null (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - February 2, 2024 Category: Biomedical Engineering Source Type: research