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.

Graph-Enhanced Emotion Neural Decoding
Brain signal-based emotion recognition has recently attracted considerable attention since it has powerful potential to be applied in human-computer interaction. To realize the emotional interaction of intelligent systems with humans, researchers have made efforts to decode human emotions from brain imaging data. The majority of current efforts use emotion similarities (e.g., emotion graphs) or brain region similarities (e.g., brain networks) to learn emotion and brain representations. However, the relationships between emotions and brain regions are not explicitly incorporated into the representation learning process. As ...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Accelerating Magnetic Resonance T1ρ Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART)
This study proposes a novel method that uses spatial patch-based and parametric group-based low-rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in $text T_{{1}rho }$ mapping. The parametric group-based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the pro...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

PA-Seg: Learning From Point Annotations for 3D Medical Image Segmentation Using Contextual Regularization and Cross Knowledge Distillation
The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Cond...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Longitudinal Assessment of Cerebral Blood Volume Variation in Human Neonates Using Ultrafast Power Doppler and Diverging Waves
In this study, we aim to measure the variations of cerebral blood volume (CBV) in human neonates during cardiac surgery, using Ultrafast Power Doppler and freehand scanning. To be clinically relevant, this method must satisfy three criteria: being able to image a wide field of view in the brain, show significant longitudinal CBV variations, and present reproducible results. To address the first point, we performed for the first time transfontanellar Ultrafast Power Doppler using a hand-held phased-array transducer with diverging waves. This increased the field of view more than threefold compared to previous studies using ...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation
Despite the recent success of deep learning models for text generation, generating clinically accurate reports remains challenging. More precisely modeling the relationships of the abnormalities revealed in an X-ray image has been found promising to enhance the clinical accuracy. In this paper, we first introduce a novel knowledge graph structure called an attributed abnormality graph (ATAG). It consists of interconnected abnormality nodes and attribute nodes for better capturing more fine-grained abnormality details. In contrast to the existing methods where the abnormality graph are constructed manually, we propose a met...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Hierarchical Bias Mitigation for Semi-Supervised Medical Image Classification
Semi-supervised learning (SSL) has demonstrated remarkable advances on medical image classification, by harvesting beneficial knowledge from abundant unlabeled samples. The pseudo labeling dominates current SSL approaches, however, it suffers from intrinsic biases within the process. In this paper, we retrospect the pseudo labeling and identify three hierarchical biases: perception bias, selection bias and confirmation bias, at feature extraction, pseudo label selection and momentum optimization stages, respectively. In this regard, we propose a HierArchical BIas miTigation (HABIT) framework to amend these biases, which co...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework
Deep learning based image enhancement models have largely improved the readability of fundus images in order to decrease the uncertainty of clinical observations and the risk of misdiagnosis. However, due to the difficulty of acquiring paired real fundus images at different qualities, most existing methods have to adopt synthetic image pairs as training data. The domain shift between the synthetic and the real images inevitably hinders the generalization of such models on clinical data. In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptati...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

A 2D Synthesized Image Improves the 3D Search for Foveated Visual Systems
Current medical imaging increasingly relies on 3D volumetric data making it difficult for radiologists to thoroughly search all regions of the volume. In some applications (e.g., Digital Breast Tomosynthesis), the volumetric data is typically paired with a synthesized 2D image (2D-S) generated from the corresponding 3D volume. We investigate how this image pairing affects the search for spatially large and small signals. Observers searched for these signals in 3D volumes, 2D-S images, and while viewing both. We hypothesize that lower spatial acuity in the observers’ visual periphery hinders the search for the small ...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Self-Distilled Hierarchical Network for Unsupervised Deformable Image Registration
Unsupervised deformable image registration benefits from progressive network structures such as Pyramid and Cascade. However, existing progressive networks only consider the single-scale deformation field in each level or stage and ignore the long-term connection across non-adjacent levels or stages. In this paper, we present a novel unsupervised learning approach named Self-Distilled Hierarchical Network (SDHNet). By decomposing the registration procedure into several iterations, SDHNet generates hierarchical deformation fields (HDFs) simultaneously in each iteration and connects different iterations utilizing the learned...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

CAT: Constrained Adversarial Training for Anatomically-Plausible Semi-Supervised Segmentation
Deep learning models for semi-supervised medical image segmentation have achieved unprecedented performance for a wide range of tasks. Despite their high accuracy, these models may however yield predictions that are considered anatomically impossible by clinicians. Moreover, incorporating complex anatomical constraints into standard deep learning frameworks remains challenging due to their non-differentiable nature. To address these limitations, we propose a Constrained Adversarial Training (CAT) method that learns how to produce anatomically plausible segmentations. Unlike approaches focusing solely on accuracy measures l...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images
CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and real-application dataset, i.e., methods trained on simulation cannot generalize well to practical data. Unsupervised MAR methods can be trained directly on practical data, but they learn MAR with indirect metrics and often perform unsatisfactorily. To tackle the domain gap problem, we propose a novel MAR method called UDAMAR based on unsupervised domain adaptation (UDA). Specifically, we introduce a UDA regularization loss into a typical image-domain supervised MAR method, whic...
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

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(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - August 1, 2023 Category: Biomedical Engineering Source Type: research

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse se...
Source: IEE Transactions on Medical Imaging - July 28, 2023 Category: Biomedical Engineering Source Type: research

Concept Graph Neural Networks for Surgical Video Understanding
Analysis of relations between objects and comprehension of abstract concepts in the surgical video is important in AI-augmented surgery. However, building models that integrate our knowledge and understanding of surgery remains a challenging endeavor. In this paper, we propose a novel way to integrate conceptual knowledge into temporal analysis tasks using temporal concept graph networks. In the proposed networks, a knowledge graph is incorporated into the temporal video analysis of surgical notions, learning the meaning of concepts and relations as they apply to the data. We demonstrate results in surgical video data for ...
Source: IEE Transactions on Medical Imaging - July 27, 2023 Category: Biomedical Engineering Source Type: research

FedDP: Dual Personalization in Federated Medical Image Segmentation
Personalized federated learning (PFL) addresses the data heterogeneity challenge faced by general federated learning (GFL). Rather than learning a single global model, with PFL a collection of models are adapted to the unique feature distribution of each site. However, current PFL methods rarely consider self-attention networks which can handle data heterogeneity by long-range dependency modeling and they do not utilize prediction inconsistencies in local models as an indicator of site uniqueness. In this paper, we propose FedDP, a novel fed erated learning scheme with ${d}$ ual ${p}$ ersonalization, which improves model p...
Source: IEE Transactions on Medical Imaging - July 26, 2023 Category: Biomedical Engineering Source Type: research