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Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

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

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Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.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 - January 1, 2021 Category: Biomedical Engineering Source Type: research

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning
This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation
Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this article, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled image...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Learning Common Harmonic Waves on Stiefel Manifold – A New Mathematical Approach for Brain Network Analyses
Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, their spatial patterns follow large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanisms of neuropathological events as they spread through the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework that ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning
We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evalua...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors
The most frequent extracranial solid tumors of childhood, named peripheral neuroblastic tumors (pNTs), are very challenging to diagnose due to their diversified categories and varying forms. Auxiliary diagnosis methods of such pediatric malignant cancers are highly needed to provide pathologists assistance and reduce the risk of misdiagnosis before treatments. In this paper, inspired by the particularity of microscopic pathology images, we integrate neural networks with the texture energy measure (TEM) and propose a novel network architecture named DetexNet (deep texture network). This method enforces the low-level represe...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks
We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and state-of-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other tech...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Augmented Reality Guided Laparoscopic Surgery of the Uterus
We present the first system for AR guided laparoscopic surgery of the uterus. This works with pre-operative MR or CT data and monocular laparoscopes, without requiring any additional interventional hardware such as optical trackers. We present novel and robust solutions to two main sub-problems: the initial registration, which is solved using a short exploratory video, and update registration, which is solved with real-time tracking-by-detection. These problems are challenging for the uterus because it is a weakly-textured, highly mobile organ that moves independently of surrounding structures. In the broader context, our ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module
We present a convolutional neural network (CNN) equipped with a novel and efficient adaptive dual attention module (ADAM) for automated skin lesion segmentation from dermoscopic images, which is an essential yet challenging step for the development of a computer-assisted skin disease diagnosis system. The proposed ADAM has three compelling characteristics. First, we integrate two global context modeling mechanisms into the ADAM, one aiming at capturing the boundary continuity of skin lesion by global average pooling while the other dealing with the shape irregularity by pixel-wise correlation. In this regard, our network, ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Internal-Illumination Photoacoustic Tomography Enhanced by a Graded-Scattering Fiber Diffuser
The penetration depth of photoacoustic imaging in biological tissues has been fundamentally limited by the strong optical attenuation when light is delivered externally through the tissue surface. To address this issue, we previously reported internal-illumination photoacoustic imaging using a customized radial-emission optical fiber diffuser, which, however, has complex fabrication, high cost, and non-uniform light emission. To overcome these shortcomings, we have developed a new type of low-cost fiber diffusers based on a graded-scattering method in which the optical scattering of the fiber diffuser is gradually increase...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks
Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of interpreting chest CT for lung cancer screening. Many studies have used CNNs to detect nodule candidates. Though such approaches have been shown to outperform the conventional image processing based methods regarding the detection accuracy, CNNs are also known to be limited to generalize on under-represented samples in the training set and prone to imperceptible noise perturbations...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy
Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for or...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Improving Blood Vessel Tortuosity Measurements via Highly Sampled Numerical Integration of the Frenet-Serret Equations
Measures of vascular tortuosity—how curved and twisted a vessel is—are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic—mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equatio...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors
Medical image segmentation is an essential task in computer-aided diagnosis. Despite their prevalence and success, deep convolutional neural networks (DCNNs) still need to be improved to produce accurate and robust enough segmentation results for clinical use. In this paper, we propose a novel and generic framework called Segmentation-Emendation-reSegmentation-Verification (SESV) to improve the accuracy of existing DCNNs in medical image segmentation, instead of designing a more accurate segmentation model. Our idea is to predict the segmentation errors produced by an existing model and then correct them. Since predicting ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation
An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an assumption that different modalities share the same anatomical structure information. However, since these methods commonly use voxel/pixel-wise cycle-consistency to regularize the mappings between modalities, high-level semantic information is not necessarily preserved. In this paper, we propose a novel anatomy-regularized repr...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation
Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consis...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Regional Lung Perfusion Analysis in Experimental ARDS by Electrical Impedance and Computed Tomography
Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. We introduce a method to robustly detect pulmonary perfusion based on indicator-enhanced electrical impedance tomography and validate it by dynamic multidetector computed tomography in two experimental models of acute respiratory distress syndrome. The acute injury was induced in a subloba...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Uncertainty Quantification in Deep MRI Reconstruction
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction. With this in mind, this study aims to quantify the uncertainty in image recovery with DL models. To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low-quality) short scans with aliasing artifacts to the diagnostic-quality ones. The VAE encodes the acquisition unce...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-af...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks
In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net w...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Performance Evaluation and Compatibility Studies of a Compact Preclinical Scanner for Simultaneous PET/MR Imaging at 7 Tesla
We present the design and performance of a new compact preclinical system combining positron emission tomography (PET) and magnetic resonance imaging (MRI) for simultaneous scans. The PET contains sixteen SiPM-based detector heads arranged in two octagons and covers an axial field of view (FOV) of 102.5 mm. Depth of interaction effects and detector’s temperature variations are compensated by the system. The PET is integrated in a dry magnet operating at 7 T. PET and MRI characteristics were assessed complying with international standards and interferences between both subsystems during simultaneous scans were address...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan
We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images
Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution
Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the ${textit k}$ -space data are sparsely sampled so that neighbouring frames can be merged to construct one temporal frame. However, this view-sharing scheme fundamentally limits the temporal resolution, and it is not possible to change the view-sharing number to achieve different spatio-temporal resolution trade-offs. Although many deep learning approaches have been recently proposed for MR reconstruction from sparse samples, the existing approaches usua...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images
In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images. Since there currently lack methods particularly for UDA instance segmentation, we first design a Domain Adaptive Mask R-CNN (DAM) as the baseline, with cross-domain feature alignment at the image and instance levels. In addition to the image- and instance-level domain discrepancy, there also exists domain bias at the semantic level in the contextual information. Next, we, therefore, design a semantic segmentation branch with a domain discrimi...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading
Diabetic Retinopathy (DR) grading is challenging due to the presence of intra-class variations, small lesions and imbalanced data distributions. The key for solving fine-grained DR grading is to find more discriminative features corresponding to subtle visual differences, such as microaneurysms, hemorrhages and soft exudates. However, small lesions are quite difficult to identify using traditional convolutional neural networks (CNNs), and an imbalanced DR data distribution will cause the model to pay too much attention to DR grades with more samples, greatly affecting the final grading performance. In this article, we focu...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Optimization of MRI Gradient Coils With Explicit Peripheral Nerve Stimulation Constraints
Peripheral Nerve Stimulation (PNS) limits the acquisition rate of Magnetic Resonance Imaging data for fast sequences employing powerful gradient systems. The PNS characteristics are currently assessed after the coil design phase in experimental stimulation studies using constructed coil prototypes. This makes it difficult to find design modifications that can reduce PNS. Here, we demonstrate a direct approach for incorporation of PNS effects into the coil optimization process. Knowledge about the interactions between the applied magnetic fields and peripheral nerves allows the optimizer to identify coil solutions that mini...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network
We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network – a cascade o...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Learning Geodesic Active Contours for Embedding Object Global Information in Segmentation CNNs
This article aims to embed the object global geometric information into a learning framework via the classical geodesic active contours (GAC). We propose a level set function (LSF) regression network, supervised by the segmentation ground truth, LSF ground truth and geodesic active contours, to not only generate the segmentation probabilistic map but also directly minimize the GAC energy functional in an end-to-end manner. With the help of geodesic active contours, the segmentation contour, embedded in the level set function, can be globally driven towards the image boundary to obtain lower energy, and the geodesic constra...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging
Alzheimer’s Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients. 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) serves as a neuroimaging modality for early diagnosis as it can reflect neural activity via measuring glucose uptake at resting-state. In this paper, we design a deep network on 18F-FDG PET modality to address the problem of AD i...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Real-Time Gain Control of PET Detectors and Evaluation With Challenging Radionuclides
We describe the methods used to combine information about multiple peaks and how this algorithm is implemented in a way that permits real-time processing on a field-programmable gate array. Simulations demonstrate rapid response time and stability. A method (“virtual scatter filter”) is also described that extracts unscattered photopeak events from phantom data and demonstrates the accuracy of the photopeak for various radionuclides that emit energies in addition to the pure 511 keV annihilation peak. Radionuclides 52 Mn, 55 Co, 64 Cu, 89 Zr, 90 Y, and 124 I are included in the study for their various forms of ...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester—a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intellig...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients
In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bS...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract
In this study, we propose a learning enabled microultrasound ( $mu $ US) system that aims to classify inflamed and non-inflamed bowel tissues. $mu $ US images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable b...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network
The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a l...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images
Early screening of PDAC (pancreatic ductal adenocarcinoma) based on plain CT (computed tomography) images is of great significance. Therefore, this work conducted a radiomics-aided diagnosis analysis of PDAC based on plain CT images. We explored a novel MSTA (multiresolution-statistical texture analysis) architecture to extract texture features and built machine learning models to classify PDACs and HPs (healthy pancreases). We also performed significance tests of differences to analyze the relationships between histopathological characteristics and texture features. The MSTA architecture originates from the analysis of hi...
Source: IEE Transactions on Medical Imaging - January 1, 2021 Category: Biomedical Engineering Source Type: research

Improving Paralysis Compensation in Photon Counting Detectors
Photon counting detectors (PCDs) are classically described as being either paralyzable or nonparalyzable. When the PCD is paralyzed, it is no longer sensitive to the detection of additional flux. A recent strategy in PCD design has been to compensate for detector paralysis by embedding specialized paralysis compensation electronics into the application-specific integrated circuit (ASIC). One such compensation mechanism is the pileup trigger, which places an additional energy bin at very high energy that is triggered only during pileup. Another compensation mechanism is the retrigger architecture, which converts a paralyzab...
Source: IEE Transactions on Medical Imaging - January 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 - January 1, 2021 Category: Biomedical Engineering Source Type: research

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

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Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research

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Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research

TechRxiv
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Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research

Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network
In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e., the Fourier slice theorem for SGCT). Based on its Fourier slice theorem and its unique data sampling in the Fourier space, a Linogram-based Fourier reconstruction method is derived for SGCT. We demonstrate that the entire Linogram reconstruction process can be embedded ...
Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research

4D Ultrafast Ultrasound Imaging of Naturally Occurring Shear Waves in the Human Heart
The objectives were to develop a novel three-dimensional technology for imaging naturally occurring shear wave (SW) propagation, demonstrate feasibility on human volunteers and quantify SW velocity in different propagation directions. Imaging of natural SWs generated by valve closures has emerged to obtain a direct measurement of cardiac stiffness. Recently, natural SW velocity was assessed in two dimensions on parasternal long axis view under the assumption of a propagation direction along the septum. However, in this approach the source localization and the complex three-dimensional propagation wave path was neglected ma...
Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research

Quantitative Classification of 3D Collagen Fiber Organization From Volumetric Images
Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distingu...
Source: IEE Transactions on Medical Imaging - December 1, 2020 Category: Biomedical Engineering Source Type: research