IEEE Transactions on Medical Imaging information for authors
(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints
Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depe...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analy...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Flexible Multi-Turn Multi-Gap Coaxial RF Coils: Design Concept and Implementation for Magnetic Resonance Imaging at 3 and 7 Tesla
Magnetic resonance has become a backbone of medical imaging but suffers from inherently low sensitivity. This can be alleviated by improved radio frequency (RF) coils. Multi-turn multi-gap coaxial coils (MTMG-CCs) introduced in this work are flexible, form-fitting RF coils extending the concept of the single-turn single-gap CC by introducing multiple cable turns and/or gaps. It is demonstrated that this enables free choice of the coil diameter, and thus, optimizing it for the application to a certain anatomical site, while operating at the self-resonance frequency. An equivalent circuit for MTMG-CCs is modeled to predict t...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Whole Brain Myelin Water Mapping in One Minute Using Tensor Dictionary Learning With Low-Rank Plus Sparse Regularization
The quantification of myelin water content in the brain can be obtained by the multi-echo $text{T}2^ast $ weighted images ( $text{T}2^ast $ WIs). To accelerate the long acquisition, a novel tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) is proposed to reconstruct the $text{T}2^ast $ WIs from the undersampled data. The proposed algorithm explores the local and nonlocal similarity and the global temporal redundancy in the real and imaginary parts of the complex relaxation signals. The joint application of the low-rank constraints on the dictionaries and the sparse constraints on the core...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

HARP-I: A Harmonic Phase Interpolation Method for the Estimation of Motion From Tagged MR Images
In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Quantitative Evaluation of an Automated Cone-Based Breast Ultrasound Scanner for MRI–3D US Image Fusion
Breast cancer is one of the most diagnosed types of cancer worldwide. Volumetric ultrasound breast imaging, combined with MRI can improve lesion detection rate, reduce examination time, and improve lesion diagnosis. However, to our knowledge, there are no 3D US breast imaging systems available that facilitate 3D US – MRI image fusion. In this paper, a novel Automated Cone-based Breast Ultrasound System (ACBUS) is introduced. The system facilitates volumetric ultrasound acquisition of the breast in a prone position without deforming it by the US transducer. Quality of ACBUS images for reconstructions at different voxe...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we c...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning
Aging and diabetes lead to protein glycation and cause dysfunction of collagen-containing tissues. The accompanying structural and functional changes of collagen significantly contribute to the development of various pathological malformations affecting the skin, blood vessels, and nerves, causing a number of complications, increasing disability risks and threat to life. In fact, no methods of non-invasive assessment of glycation and associated metabolic processes in biotissues or prediction of possible skin complications, e.g., ulcers, currently exist for endocrinologists and clinical diagnosis. In this publication, utili...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Contrast-Attentive Thoracic Disease Recognition With Dual-Weighting Graph Reasoning
Automatic thoracic disease diagnosis is a rising research topic in the medical imaging community, with many potential applications. However, the inconsistent appearances and high complexities of various lesions in chest X-rays currently hinder the development of a reliable and robust intelligent diagnosis system. Attending to the high-probability abnormal regions and exploiting the priori of a related knowledge graph offers one promising route to addressing these issues. As such, in this paper, we propose two contrastive abnormal attention models and a dual-weighting graph convolution to improve the performance of thoracic...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation n...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images
Speckle noise is the main cause of poor optical coherence tomography (OCT) image quality. Convolutional neural networks (CNNs) have shown remarkable performances for speckle noise reduction. However, speckle noise denoising still meets great challenges because the deep learning-based methods need a large amount of labeled data whose acquisition is time-consuming or expensive. Besides, many CNNs-based methods design complex structure based networks with lots of parameters to improve the denoising performance, which consume hardware resources severely and are prone to overfitting. To solve these problems, we propose a novel ...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations
Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based st...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Comparison of 16-Channel Asymmetric Sleeve Antenna and Dipole Antenna Transceiver Arrays at 10.5 Tesla MRI
Multi-element transmit arrays with low peak 10 g specific absorption rate (SAR) and high SAR efficiency (defined as ( $text{B}_{{1}}^{+}/surd _{text {peak}}$ SAR $_{text {10g}}{)}$ are essential for ultra-high field (UHF) magnetic resonance imaging (MRI) applications. Recently, the adaptation of dipole antennas used as MRI coil elements in multi-channel arrays has provided the community with a technological solution capable of producing uniform images and low SAR efficiency at these high field strengths. However, human head-sized arrays consisting of dipole elements have a practical limitation to the number of channels tha...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Learn to Threshold: ThresholdNet With Confidence-Guided Manifold Mixup for Polyp Segmentation
The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation, however, are inadequate due to the limited annotated dataset and the class imbalance problems. Moreover, these methods obtained the final polyp segmentation results by simply thresholding the likelihood maps at an eclectic and equivalent value (often set to 0.5). In this paper, we propose a novel ThresholdNet with a confidence-guided manifold mixup (CGMMix) data augmentation method, mainly for addressing the aforementioned issues in polyp segmenta...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segm...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a n...
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Parametric Sequential Method for MRI-Based Wall Shear Stress Quantification
We present a parametric sequential method for MRI-based WSS quantification consisting of a geometry identification and a subsequent approximation of the velocity field. This work focuses on its validation, investigating well controlled high-resolution in vitro measurements of turbulent stationary flows and physiological pulsatile flows in phantoms. Initial tests for in vivo 2D PC-MRI data of the ascending aorta of three volunteers demonstrate basic applicability of the method to in vivo. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

IEEE Transactions on Medical Imaging publication information
(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

Table of contents
(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - April 1, 2021 Category: Biomedical Engineering Source Type: research

IEEE Transactions on Medical Imaging information for authors
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

TechRxiv
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Diagnostic Regions Attention Network (DRA-Net) for Histopathology WSI Recommendation and Retrieval
The development of whole slide imaging techniques and online digital pathology platforms have accelerated the popularization of telepathology for remote tumor diagnoses. During a diagnosis, the behavior information of the pathologist can be recorded by the platform and then archived with the digital case. The browsing path of the pathologist on the WSI is one of the valuable information in the digital database because the image content within the path is expected to be highly correlated with the diagnosis report of the pathologist. In this article, we proposed a novel approach for computer-assisted cancer diagnosis named s...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking
Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in the cardiovascular system or shear-wave elastography. The accuracy achievable with these motion estimation techniques is strongly contingent upon two contradictory requirements: a high quality of consecutive frames and a high frame rate. Indeed, the image quality can usually be improved by increasing the number of steered ultrafast acquisitions, but at the expense of a red...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation
In this study, we provide new insights on the problem of overfitting under class imbalance by inspecting the network behavior. We find empirically that when training with limited data and strong class imbalance, at test time the distribution of logit activations may shift across the decision boundary, while samples of the well-represented class seem unaffected. This bias leads to a systematic under-segmentation of small structures. This phenomenon is consistently observed for different databases, tasks and network architectures. To tackle this problem, we introduce new asymmetric variants of popular loss functions and regu...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Development and Evaluation of the Fourier Spectral Distortion Metric
A spatial resolution metric is presented for tomosynthesis. The Fourier spectral distortion metric (FSD) was developed to evaluate specific resolution properties of different imaging techniques for digital tomosynthesis using a star pattern image to plot modulation in the frequency domain. The FSD samples the spatial resolution of a star-pattern image tangentially over an acute angle and for a range of spatial frequencies in a 2D image or 3D image reconstruction slice. The FSD graph portrays all frequencies present in a star pattern quadrant. In addition to the fundamental input frequency of the star pattern, the FSD graph...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver Using Multi-Labelled Content-Based Image Retrieval
Laparoscopic Ultrasound (LUS) is recommended as a standard-of-care when performing laparoscopic liver resections as it images sub-surface structures such as tumours and major vessels. Given that LUS probes are difficult to handle and some tumours are iso-echoic, registration of LUS images to a pre-operative CT has been proposed as an image-guidance method. This registration problem is particularly challenging due to the small field of view of LUS, and usually depends on both a manual initialisation and tracking to compose a volume, hindering clinical translation. In this paper, we extend a proposed registration approach us...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection
Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select envi...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Foveated Model Observers for Visual Search in 3D Medical Images
Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space to be scrutinized. Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f2.8 filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search. We investigated two different signal types t...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating Gantry-Type CT Scanner
CT scan by use of a beam-filter placed between the x-ray source and the patient allows a single-scan low-dose dual-energy imaging with a minimal hardware modification to the existing CT systems. We have earlier demonstrated the feasibility of such imaging method with a multi-slit beam-filter reciprocating along the direction perpendicular to the CT rotation axis in a cone-beam CT system. However, such method would face mechanical challenges when the beam-filter is supposed to cooperate with a fast-rotating gantry in a diagnostic CT system. In this work, we propose a new scanning method and associated image reconstruction a...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Modeling and Enhancing Low-Quality Retinal Fundus Images
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this article, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inferior-quality factors, inclu...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI
Multi–parametric prostate MRI (mpMRI) is a powerful tool to diagnose prostate cancer, though difficult to interpret even for experienced radiologists. A common radiological procedure is to compare a magnetic resonance image with similarly diagnosed cases. To assist the radiological image interpretation process, computerized Content–Based Image Retrieval systems (CBIRs) can therefore be employed to improve the reporting workflow and increase its accuracy. In this article, we propose a new, supervised siamese deep learning architecture able to handle multi–modal and multi–view MR images with similar P...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Dual-Energy X-Ray Dark-Field Material Decomposition
Dual-energy imaging is a clinically well-established technique that offers several advantages over conventional X-ray imaging. By performing measurements with two distinct X-ray spectra, differences in energy-dependent attenuation are exploited to obtain material-specific information. This information is used in various imaging applications to improve clinical diagnosis. In recent years, grating-based X-ray dark-field imaging has received increasing attention in the imaging community. The X-ray dark-field signal originates from ultra small-angle scattering within an object and thus provides information about the microstruc...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Radio-Frequency Vector Magnetic Field Mapping in Magnetic Resonance Imaging
A method is presented to measure the radio-frequency (RF) vector magnetic field inside an object using magnetic resonance imaging (MRI). Conventional “ $text{B}_{{1}}$ mapping” in MRI can measure the proton co-rotating component ( $text{B}_{{1}}^{+}{)}$ of the RF field produced by a transmit coil. Here we show that by repeating $text{B}_{{1}}^{+}$ mapping on the same object and coil at multiple (8) specific orientations with respect to the main magnet, the magnitudes and relative phases of all (x, y, z) Cartesian components of the RF field can be determined unambiguously. We demonstrate the method on a circular...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

A Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study
With the development of neuroimaging techniques, a growing amount of multi-modal brain imaging data are collected, facilitating comprehensive study of the brain. In this paper, we jointly analyzed functional magnetic resonance imaging (fMRI) collected under different paradigms in order to understand cognitive behaviors of an individual. To this end, we proposed a novel multi-view learning algorithm called structure-enforced collaborative regression (SCoRe) to extract co-expressed discriminative brain regions under the guidance of anatomical structure of the brain. An advantage of SCoRe over its predecessor collaborative re...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Hierarchical Extraction of Functional Connectivity Components in Human Brain Using Resting-State fMRI
The study of functional networks of the human brain has been of significant interest in cognitive neuroscience for over two decades, albeit they are typically extracted at a single scale using various methods, including decompositions like ICA. However, since numerous studies have suggested that the functional organization of the brain is hierarchical, analogous decompositions might better capture functional connectivity patterns. Moreover, hierarchical decompositions can efficiently reduce the very high dimensionality of functional connectivity data. This paper provides a novel method for the extraction of hierarchical co...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Interpolation and Averaging of Diffusion MRI Multi-Compartment Models
Multi-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the ...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

LCANet: Learnable Connected Attention Network for Human Identification Using Dental Images
Forensic odontology is regarded as an important branch of forensics dealing with human identification based on dental identification. This paper proposes a novel method that uses deep convolution neural networks to assist in human identification by automatically and accurately matching 2-D panoramic dental X-ray images. Designed as a top-down architecture, the network incorporates an improved channel attention module and a learnable connected module to better extract features for matching. By integrating associated features among all channel maps, the channel attention module can selectively emphasize interdependent channe...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers
A critical challenge in using longitudinal neuroimaging data to study the progressions of Alzheimer’s Disease (AD) is the varied number of missing records of the patients during the course when AD develops. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation with fixed length for imaging biomarkers, which aims to simultaneously capture the information conveyed by both baseline neuroimaging record and progressive variations characterized by varied counts of available follow-up records over time. Because the learned biomarker representations are a set of fixed-length...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection
Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a on...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method
This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a ...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings
Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. T...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Short-Term Lesion Change Detection for Melanoma Screening With Novel Siamese Neural Network
Short-term monitoring of lesion changes has been a widely accepted clinical guideline for melanoma screening. When there is a significant change of a melanocytic lesion at three months, the lesion will be excised to exclude melanoma. However, the decision on change or no-change heavily depends on the experience and bias of individual clinicians, which is subjective. For the first time, a novel deep learning based method is developed in this paper for automatically detecting short-term lesion changes in melanoma screening. The lesion change detection is formulated as a task measuring the similarity between two dermoscopy im...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Super-Resolution Ultrasound Localization Microscopy Through Deep Learning
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultraso...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in the retina. Computer-aided DR diagnosis has become a promising tool for the early detection and severity grading of DR, due to the great success of deep learning. However, most current DR diagnosis systems do not achieve satisfactory performance or interpretability for ophthalmologists, due to the lack of training data with consistent and fine-grained annotations. To address this problem, we construct a large fine-grained annotated DR dataset con...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning
High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the app...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy
This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the “dilated CNN + RN”, a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation
We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no (unsupervised) annotations are available for this specific modality. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, which non-linearly aligns the anatomical ...
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research

IEEE Transactions on Medical Imaging publication information
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - March 1, 2021 Category: Biomedical Engineering Source Type: research