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.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction
Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently no...
Source: IEE Transactions on Medical Imaging - September 8, 2023 Category: Biomedical Engineering Source Type: research

Point-Supervised Single-Cell Segmentation via Collaborative Knowledge Sharing
Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the only available training label is the rough locations of individual cells. The specific problem is of practical interest due to the widely available nuclei counter-stain data in biomedical literature, from which the cell locations can be derived programmatically. Of more...
Source: IEE Transactions on Medical Imaging - September 7, 2023 Category: Biomedical Engineering Source Type: research

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data
Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two agents–a radiologist and a general practitioner – we predict prognosis with two transformer-based components that...
Source: IEE Transactions on Medical Imaging - September 6, 2023 Category: Biomedical Engineering Source Type: research

Stable Deep MRI Reconstruction Using Generative Priors
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classica...
Source: IEE Transactions on Medical Imaging - September 1, 2023 Category: Biomedical Engineering Source Type: research

OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning
X-ray computed tomography (CT) has been broadly adopted in clinical applications for disease diagnosis and image-guided interventions. However, metals within patients always cause unfavorable artifacts in the recovered CT images. Albeit attaining promising reconstruction results for this metal artifact reduction (MAR) task, most of the existing deep-learning-based approaches have some limitations. The critical issue is that most of these methods have not fully exploited the important prior knowledge underlying this specific MAR task. Therefore, in this paper, we carefully investigate the inherent characteristics of metal a...
Source: IEE Transactions on Medical Imaging - September 1, 2023 Category: Biomedical Engineering Source Type: research

Kernel Attention Transformer for Histopathology Whole Slide Image Analysis and Assistant Cancer Diagnosis
Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Tran...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning
Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-learning algorithms have been developed for automatic retinal fundus image analysis. However, existing methods are usually data-driven, which rarely consider the costs associated with fundus image collection and annotation, along with the class-imbalanced distribution that arises from the relative scarcity of disease-positive individuals in the population. Semi-supervised learning on class-imbalanced data, despite a realistic problem, has been relatively little studied. To fill the existing research gap, we explore generative adversar...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning
Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated ...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Characterization and Assessment of Projection Probability Density Function and Enhanced Sampling in Self-Collimation SPECT
We have recently reported a self-collimation SPECT (SC-SPECT) design concept that constructs sensitive detectors in a multi-ring interspaced mosaic architecture to simultaneously improve system spatial resolution and sensitivity. In this work, through numerical and Monte-Carlo simulation studies, we investigate this new design concept by analyzing its projection probability density functions (PPDF) and the effects of enhanced sampling, i.e. having rotational and translational object movements during imaging. We first quantitatively characterize PPDFs by their widths and edge slopes. Then we compare the PPDFs of an SC-SPECT...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

GRAB-Net: Graph-Based Boundary-Aware Network for Medical Point Cloud Segmentation
Point cloud segmentation is fundamental in many medical applications, such as aneurysm clipping and orthodontic planning. Recent methods mainly focus on designing powerful local feature extractors and generally overlook the segmentation around the boundaries between objects, which is extremely harmful to the clinical practice and degenerates the overall segmentation performance. To remedy this problem, we propose a GRAph-based Boundary-aware Network (GRAB-Net) with three paradigms, Graph-based Boundary-perception Module (GBM), Outer-boundary Context-assignment Module (OCM), and Inner-boundary Feature-rectification Module (...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation
Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important for segmentation task to build global context dependencies. The Transformers can establish long-range dependencies among pixels by self-attention, providing a supplement to the local convolution. In addition, multi-scale feature fusion and feature selection are crucial for medical image segmentation tasks, which is ignored by Transformers. However, it is challenging to dir...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures
Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and ca...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet
U-Nets have achieved tremendous success in medical image segmentation. Nevertheless, it may have limitations in global (long-range) contextual interactions and edge-detail preservation. In contrast, the Transformer module has an excellent ability to capture long-range dependencies by leveraging the self-attention mechanism into the encoder. Although the Transformer module was born to model the long-range dependency on the extracted feature maps, it still suffers high computational and spatial complexities in processing high-resolution 3D feature maps. This motivates us to design an efficient Transformer-based UNet model an...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

Optical Co-Registration Method of Triaxial OPM-MEG and MRI
In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the ${X}$ -and ${Y}$ orientations and less than 1.6° in the ${Z}$ orientation. Furthermore, we assessed the influence of co-registration...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images
Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid...
Source: IEE Transactions on Medical Imaging - August 31, 2023 Category: Biomedical Engineering Source Type: research