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.

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects
3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC),...
Source: IEE Transactions on Medical Imaging - August 17, 2023 Category: Biomedical Engineering Source Type: research

FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is norm...
Source: IEE Transactions on Medical Imaging - August 17, 2023 Category: Biomedical Engineering Source Type: research

ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI
Tractography can generate millions of complex curvilinear fibers (streamlines) in 3D that exhibit the geometry of white matter pathways in the brain. Common approaches to analyzing white matter connectivity are based on adjacency matrices that quantify connection strength but do not account for any topological information. A critical element in neurological and developmental disorders is the topological deterioration and irregularities in streamlines. In this paper, we propose a novel Reeb graph-based method “ReeBundle” that efficiently encodes the topology and geometry of white matter fibers. Given the trajectories of...
Source: IEE Transactions on Medical Imaging - August 17, 2023 Category: Biomedical Engineering Source Type: research

Transformer-Based Spatio-Temporal Analysis for Classification of Aortic Stenosis Severity From Echocardiography Cine Series
We present an empirical study on how the model learns phases of the heart cycle without any supervision and frame-level annotations. Our architecture outperforms state-of-the-art results on a private and a public dataset, achieving 95.2% and 91.5% in AS detection, and 78.1% and 83.8% in AS severity classification on the private and public datasets, respectively. Notably, due to the lack of a large public video dataset for AS, we made slight adjustments to our architecture for the public dataset. Furthermore, our method addresses common problems in training deep networks with clinical ultrasound data, such as a low signal-t...
Source: IEE Transactions on Medical Imaging - August 15, 2023 Category: Biomedical Engineering Source Type: research

A Causality-Driven Graph Convolutional Network for Postural Abnormality Diagnosis in Parkinsonians
Abnormal posture is a common movement disorder in the progress of Parkinson’s disease (PD), and this abnormality can increase the risk of falls or even disabilities. The conventional assessment approach depends on the judgment of well-trained experts via canonical scales. However, this approach requires extensive clinical expertise and is highly subjective. Considering the potential of quantitative susceptibility mapping (QSM) in PD diagnosis, this study explored the QSM-based method for the automated classification between PD patients with and without postural abnormalities. Nevertheless, a major challenge is that unsta...
Source: IEE Transactions on Medical Imaging - August 15, 2023 Category: Biomedical Engineering Source Type: research

Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks
In this study, a novel MCMC method that employs a generative adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and evaluated. The MCMC-GAN method was quantitatively validated by use of test-cases for which reference solutions were available. The results demonstrate that the MCMC-GAN method can extend the domain of applicability of MCMC methods for conducting IO analyses of medical imaging systems. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - August 14, 2023 Category: Biomedical Engineering Source Type: research

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography
Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-leve...
Source: IEE Transactions on Medical Imaging - August 11, 2023 Category: Biomedical Engineering Source Type: research

Hierarchical Knowledge Guided Learning for Real-World Retinal Disease Recognition
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited number of samples), which results in a challenging long-tailed learning scenario. Some recently published datasets in ophthalmology AI consist of more than 40 kinds of retinal diseases with complex abnormalities and variable morbidity. Nevertheless, more than 30 conditions are rarely seen in global patient cohorts. From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where...
Source: IEE Transactions on Medical Imaging - August 7, 2023 Category: Biomedical Engineering Source Type: research

A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers
We report registration pose error, target registration error (TRE) and success rate (SR) with a threshold of 10mm for mean TRE. For the pelvis anatomy, the median TRE of ProST followed by CMAES is 4.4mm with a SR of 65.6% in simulation, and 2.2mm with a SR of 73.2% in real data. The CMAES SRs without using ProST registration are 28.5% and 36.0% in simulation and real data, respectively. Our results suggest that the proposed ProST network learns a practical similarity function, which vastly extends the capture range of conventional intensity-based 2D/3D registration. We believe that the unique differentiable property of Pro...
Source: IEE Transactions on Medical Imaging - August 7, 2023 Category: Biomedical Engineering Source Type: research

Learned Tensor Low-CP-Rank and Bloch Response Manifold Priors for Non-Cartesian MRF Reconstruction
Magnetic resonance fingerprinting (MRF) can rapidly perform simultaneous imaging of multiple tissue parameters. However, the rapid acquisition schemes used in MRF inevitably introduce aliasing artifacts in the recovered tissue fingerprints, reducing the accuracy of the predicted parameter maps. Current regularized reconstruction methods are based on iterative procedures which are usually time-consuming. In addition, most of the current deep learning-based methods for MRF often lack interpretability owing to the black-box nature, and most deep learning-based methods are not applicable for non-Cartesian scenarios, which limi...
Source: IEE Transactions on Medical Imaging - August 7, 2023 Category: Biomedical Engineering Source Type: research

Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities
Accurate segmentation of brain tumors is of critical importance in clinical assessment and treatment planning, which requires multiple MR modalities providing complementary information. However, due to practical limits, one or more modalities may be missing in real scenarios. To tackle this problem, existing methods need to train multiple networks or a unified but fixed network for various possible missing modality cases, which leads to high computational burdens or sub-optimal performance. In this paper, we propose a unified and adaptive multi-modal MR image synthesis method, and further apply it to tumor segmentation wit...
Source: IEE Transactions on Medical Imaging - August 4, 2023 Category: Biomedical Engineering Source Type: research

DEQ-MPI: A Deep Equilibrium Reconstruction With Learned Consistency for Magnetic Particle Imaging
Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that is used to reconstruct data from subsequent scans. The ill-posed reconstruction problem can be solved by simultaneously enforcing data consistency based on the SM and regularizing the solution based on an image prior. Traditional hand-crafted priors cannot capture the complex attributes of MPI images, whereas recent MPI methods based on learned priors can suffer from extensive inference times or limited generalization performance. Here, we introduce ...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation
The segmentation of blurred cell boundaries in cornea endothelium microscope images is challenging, which affects the clinical parameter estimation accuracy. Existing deep learning methods only consider pixel-wise classification accuracy and lack of utilization of cell structure knowledge. Therefore, the segmentation of the blurred cell boundary is discontinuous. This paper proposes a structural prior guided network (SPG-Net) for corneal endothelium cell segmentation. We first employ a hybrid transformer convolution backbone to capture more global context. Then, we use Feature Enhancement (FE) module to improve the represe...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Federated Domain Adaptation via Transformer for Multi-Site Alzheimer’s Disease Diagnosis
In multi-site studies of Alzheimer’s disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transform...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Hybrid Graph Convolutional Network With Online Masked Autoencoder for Robust Multimodal Cancer Survival Prediction
Cancer survival prediction requires exploiting related multimodal information (e.g., pathological, clinical and genomic features, etc.) and it is even more challenging in clinical practices due to the incompleteness of patient’s multimodal data. Furthermore, existing methods lack sufficient intra- and inter-modal interactions, and suffer from significant performance degradation caused by missing modalities. This manuscript proposes a novel hybrid graph convolutional network, entitled HGCN, which is equipped with an online masked autoencoder paradigm for robust multimodal cancer survival prediction. Particularly, we ...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research