4,712 results match your criteria IEEE Transactions on Medical Imaging[Journal]


Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net.

IEEE Trans Med Imaging 2019 Apr 16. Epub 2019 Apr 16.

Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features. To address these difficulties, we introduce a Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911588DOI Listing
April 2019
1 Read

An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography.

IEEE Trans Med Imaging 2019 Apr 16. Epub 2019 Apr 16.

Previous work has shown that total variation superiorization (TVS) improves reconstructed image quality in proton computed tomography (pCT). The structure of the TVS algorithm has evolved since then and this work investigated if this new algorithmic structure provides additional benefits to pCT image quality. Structural and parametric changes introduced to the original TVS algorithm included: (1) inclusion or exclusion of TV reduction requirement, (2) a variable number, N, of TV perturbation steps per feasibility-seeking iteration, and (3) introduction of a perturbation kernel 0 < α < 1. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911482DOI Listing

Quantification of Ventilation and Gas Uptake in Free-Breathing Mice with Hyperpolarized 129Xe MRI.

IEEE Trans Med Imaging 2019 Apr 15. Epub 2019 Apr 15.

Hyperpolarized 129Xe magnetic resonance imaging is a powerful modality capable of assessing lung structure and function. While it has shown promise as a clinical tool for longitudinal assessment of lung function, its utility as an investigative tool for animal models of pulmonary diseases is limited by the necessity of invasive intubation and mechanical ventilation procedures. In this study, we overcame this limitation by developing a gas delivery system and implementing a set of imaging schemes to acquire high-resolution gas-and dissolvedphase images in free-breathing mice. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911293DOI Listing
April 2019
3 Reads

Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods.

IEEE Trans Med Imaging 2019 Apr 15. Epub 2019 Apr 15.

It is widely accepted that optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an observer to perform a specific task such as detection or estimation of a signal (e.g. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911211DOI Listing

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.

IEEE Trans Med Imaging 2019 Apr 15. Epub 2019 Apr 15.

Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. Read More

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http://dx.doi.org/10.1109/TMI.2019.2911203DOI Listing

SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model.

IEEE Trans Med Imaging 2019 Apr 12. Epub 2019 Apr 12.

In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely degrades the vascular visibility, particularly in low- SNR regions. To mathematically characterize the decorrelation-SNR dependence of OCT signals, we developed a multi-variate time series (MVTS) model. Based on the model, we derived a universal asymptotic linear relation of decorrelation to inverse SNR (iSNR), with the variance in static and noise regions determined by the average kernel size. Read More

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http://dx.doi.org/10.1109/TMI.2019.2910871DOI Listing

Learning to Reconstruct Computed Tomography (CT) Images Directly from Sinogram Data under A Variety of Data Acquisition Conditions.

IEEE Trans Med Imaging 2019 Apr 11. Epub 2019 Apr 11.

Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e. Read More

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http://dx.doi.org/10.1109/TMI.2019.2910760DOI Listing

Multi-exponential Relaxometry using l1-regularized Iterative NNLS (MERLIN) with Application to Myelin Water Fraction Imaging: Supplementary Material.

IEEE Trans Med Imaging 2019 Apr 11. Epub 2019 Apr 11.

Here, we provide additional results supporting the use of MERLIN [1]; a novel l1-regularized iterative non-negative least-squares method for multi-exponential relaxometry. Read More

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http://dx.doi.org/10.1109/TMI.2019.2910386DOI Listing

Group-sparsity-based super-resolution dipole orientation mapping (GS-SDOM).

IEEE Trans Med Imaging 2019 Apr 11. Epub 2019 Apr 11.

The dipole orientation of fluorophores could be resolved by fluorescence polarization microscopy (FPM), which in turn reveals structural specificity for the labeled organelles. Conventional FPM can detect only the averaged fluorescence anisotropy collected from dipoles within the diffraction-limited volume. Super-resolution dipole orientation mapping (SDOM) method, which applies sparse deconvolution and least square estimation to fluorescence polarization modulation data, achieves the dipole orientation measurement within a sub-diffraction focal area. Read More

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http://dx.doi.org/10.1109/TMI.2019.2910221DOI Listing
April 2019
1 Read
3.390 Impact Factor

Detection of Regional Mechanical Activation of the Left Ventricular Myocardium using High Frame Rate Ultrasound Imaging.

IEEE Trans Med Imaging 2019 Apr 9. Epub 2019 Apr 9.

We have investigated the feasibility of noninvasive mapping of mechanical activation patterns in the left ventricular (LV) myocardium using high frame rate ultrasound imaging for the purpose of detecting conduction abnormalities. Five anesthetized, open chest dogs with implanted combined sonomicrometry and electromyography (EMG) crystals were studied. The animals were paced from specified locations of the heart, while crystal and ultrasound data were acquired. Read More

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http://dx.doi.org/10.1109/TMI.2019.2909358DOI Listing
April 2019
2 Reads

An evaluation of atlas selection methods for atlas-based automatic segmentation in radiotherapy treatment planning.

IEEE Trans Med Imaging 2019 Apr 9. Epub 2019 Apr 9.

Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This work presents an analysis of atlas selection methods in the context of radiotherapy treatment planning. Read More

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http://dx.doi.org/10.1109/TMI.2019.2907072DOI Listing
April 2019
2 Reads

Ultrafast 3D Ultrasound Localization Microscopy using a 32×32 Matrix Array.

IEEE Trans Med Imaging 2019 Apr 1. Epub 2019 Apr 1.

Ultrasound Localization Microscopy can map blood vessels with a resolution much smaller than the wavelength by localizing microbubbles. Current implementations of the technique are limited to 2-D planes or small fields of view in 3D. These suffer from minute-long acquisitions, out-of-plane microbubbles and tissue motion. Read More

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http://dx.doi.org/10.1109/TMI.2018.2890358DOI Listing

Free-breathing & ungated cardiac MRI using iterative SToRM (i-SToRM).

IEEE Trans Med Imaging 2019 Mar 28. Epub 2019 Mar 28.

We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SToRM (SmooThness Regularization on Manifolds) scheme that exploits the global manifold structure of images in the dataset. Since the manifold structure of the patches varies depending on the spatial location and is often considerably simpler than the global one, this approach significantly reduces the data demand, facilitating the recovery from shorter scans. Read More

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http://dx.doi.org/10.1109/TMI.2019.2908140DOI Listing

A Statistical Model for Rigid Image Registration Performance: The Influence of Soft-Tissue Deformation as a Confounding Noise Source.

IEEE Trans Med Imaging 2019 Mar 27. Epub 2019 Mar 27.

Soft-tissue deformation presents a confounding factor to rigid image registration by introducing image content inconsistent with the underlying motion model, presenting non-correspondent structure with potentially high power, and creating local minima that challenge iterative optimization. In this work, we introduce a model for registration performance that includes deformable soft tissue as a power-law noise distribution within a statistical framework describing the Cramer-Rao lower bound (CRLB) and root-mean-squared error (RMSE) in registration performance. The model incorporates both cross-correlation and gradient-based similarity metrics and was tested in application to 3D-2D (CT-to-radiograph) and 3D-3D (CT-to-CT) image registration. Read More

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http://dx.doi.org/10.1109/TMI.2019.2907868DOI Listing
March 2019
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Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning.

IEEE Trans Med Imaging 2019 Mar 27. Epub 2019 Mar 27.

Deep Convolutional Neural Networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is difficult, expensive and time consuming to obtain in medical settings. Active Learning (AL) algorithms can facilitate training CNN models by proposing a small number of the most informative data samples to be annotated to achieve a rapid increase in performance. Read More

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http://dx.doi.org/10.1109/TMI.2019.2907805DOI Listing

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks.

IEEE Trans Med Imaging 2019 Mar 19. Epub 2019 Mar 19.

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one localscale network (L-Net). Read More

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http://dx.doi.org/10.1109/TMI.2019.2905841DOI Listing
March 2019
1 Read

Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.

IEEE Trans Med Imaging 2019 Mar 25. Epub 2019 Mar 25.

Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural network (CNN) and compressed sensing (CS) or sparse coding (SC) for end-to-end training. We also derive, for the first time, a backpropagation rule, which is applicable to train any algorithm that implements a sparse code recovery layer. Read More

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http://dx.doi.org/10.1109/TMI.2019.2907093DOI Listing

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

IEEE Trans Med Imaging 2019 Mar 22. Epub 2019 Mar 22.

Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. Read More

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http://dx.doi.org/10.1109/TMI.2019.2907049DOI Listing
March 2019
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Convolutional Sparse Coding for Compressed Sensing CT Reconstruction.

IEEE Trans Med Imaging 2019 Mar 22. Epub 2019 Mar 22.

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906853DOI Listing
March 2019
3.390 Impact Factor

Factor analysis of dynamic PET images: beyond Gaussian noise.

IEEE Trans Med Imaging 2019 Mar 21. Epub 2019 Mar 21.

Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if considered with respect to suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Poissonian nature of the count-rates. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906828DOI Listing

Hippocampus Segmentation based on Iterative Local Linear Mapping with Representative and Local Structure-preserved Feature Embedding.

IEEE Trans Med Imaging 2019 Mar 21. Epub 2019 Mar 21.

Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906727DOI Listing

Arterial Spin Labeling Images Synthesis from sMRI using Unbalanced Deep Discriminant Learning.

IEEE Trans Med Imaging 2019 Mar 21. Epub 2019 Mar 21.

Adequate medical images are often indispensable in contemporary deep learning-based medical imaging studies, although the acquisition of certain image modalities may be limited due to several issues including high costs, patients issues, etc. However, thanks to recent advances in deep learning techniques, the above tough problem can be substantially alleviated by medical images synthesis, by which various modalities including T1 / T2 / DTI MRI images, PET images, cardiac ultrasound images, retinal images, etc., have already been synthesized. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906677DOI Listing

Noninvasive Reconstruction of Transmural Transmembrane Potential with Simultaneous Estimation of Prior Model Error.

IEEE Trans Med Imaging 2019 Mar 20. Epub 2019 Mar 20.

To reconstruct electrical activity in the heart from body-surface electrocardiograms (ECGs) is an ill-posed inverse problem. Electrophysiological models have been found effective in regularizing these inverse problems by incorporating a priori knowledge about how the electrical potential in the heart propagates over time. However, these models suffer from model errors arising from, for example, parameters associated with tissue properties and the earliest sites of excitation. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906600DOI Listing
March 2019
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Shape Optimization of an Electric Dipole Array for 7 Tesla Neuroimaging.

IEEE Trans Med Imaging 2019 Mar 20. Epub 2019 Mar 20.

Radio-frequency (RF) arrays constructed using electric dipoles have potential benefits for transmit and receive applications using ultra-high field (UHF) MRI. This study examines some of the implementation barriers regarding dipole RF arrays for human head imaging at 7 T. The dipole array was constructed with conformal, meandered dipoles with dimensions selected utilizing an evolutionary-based optimization routine to shape-optimize the dipole structure. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906507DOI Listing

Correlation-weighted sparse representation for robust liver DCE-MRI decomposition registration.

IEEE Trans Med Imaging 2019 Mar 20. Epub 2019 Mar 20.

Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast agent from original liver DCE-MR images. Read More

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http://dx.doi.org/10.1109/TMI.2019.2906493DOI Listing

A novel weakly supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE Trans Med Imaging 2019 Mar 20. Epub 2019 Mar 20.

Obtaining the complete segmentation map of retinal lesions is the first step towards an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a pixel level restricts the ability of deep segmentation neural networks to generalize over large databases. In this paper, we propose a novel approach for training a convolutional multi-task architecture with supervised learning and reinforcing it with weakly supervised learning. Read More

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https://ieeexplore.ieee.org/document/8672120/
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http://dx.doi.org/10.1109/TMI.2019.2906319DOI Listing
March 2019
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TETRIS: Template Transformer Networks for Image Segmentation with Shape Priors.

IEEE Trans Med Imaging 2019 Mar 22. Epub 2019 Mar 22.

In this paper we introduce and compare different approaches for incorporating shape prior information into neural network based image segmentation. Specifically, we introduce the concept of template transformer networks where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors and is free of discretisation artefacts by providing a soft partial volume segmentation. Read More

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http://dx.doi.org/10.1109/TMI.2019.2905990DOI Listing

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG-MRI.

IEEE Trans Med Imaging 2019 Mar 20. Epub 2019 Mar 20.

With a hybrid MEG-MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. Read More

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https://ieeexplore.ieee.org/document/8672109/
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http://dx.doi.org/10.1109/TMI.2019.2905934DOI Listing
March 2019
5 Reads

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge.

IEEE Trans Med Imaging 2019 Mar 19. Epub 2019 Mar 19.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. Read More

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http://dx.doi.org/10.1109/TMI.2019.2905770DOI Listing
March 2019
1 Read

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features.

IEEE Trans Med Imaging 2019 Mar 18. Epub 2019 Mar 18.

In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Read More

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http://dx.doi.org/10.1109/TMI.2019.2905917DOI Listing

B-spline based sharp feature preserving shape reconstruction approach for electrical impedance tomography.

IEEE Trans Med Imaging 2019 Mar 18. Epub 2019 Mar 18.

This paper presents a B-spline based shape reconstruction approach for electrical impedance tomography (EIT). In the proposed approach, the conductivity distribution to be reconstructed is assumed to be piecewise constant. The geometry of the inclusions is parameterized using B-spline curves, and the EIT forward solver is modified as a set of control points representing the inclusions' boundary to the data on the domain boundary. Read More

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http://dx.doi.org/10.1109/TMI.2019.2905245DOI Listing
March 2019
3 Reads

3D Multi-resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain.

IEEE Trans Med Imaging 2019 Mar 15. Epub 2019 Mar 15.

The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. Read More

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http://dx.doi.org/10.1109/TMI.2019.2904762DOI Listing

Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations.

IEEE Trans Med Imaging 2019 Mar 12. Epub 2019 Mar 12.

Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. Read More

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https://ieeexplore.ieee.org/document/8666168/
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http://dx.doi.org/10.1109/TMI.2019.2904555DOI Listing
March 2019
9 Reads
3.390 Impact Factor

CE-Net: Context Encoder Network for 2D Medical Image Segmentation.

IEEE Trans Med Imaging 2019 Mar 7. Epub 2019 Mar 7.

Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, etc. Previously, U-net based approaches have been proposed. Read More

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https://ieeexplore.ieee.org/document/8662594/
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http://dx.doi.org/10.1109/TMI.2019.2903562DOI Listing
March 2019
21 Reads

Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment.

IEEE Trans Med Imaging 2019 Mar 7. Epub 2019 Mar 7.

Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labelled database and a large unlabelled database, the goal is to train a powerful classi?er. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labelled and large unlabelled database. Various studies have shown that glaucoma can be monitored by analyzing the optic disc and its surroundings, for that reason the images used in this work were automatically cropped around the optic disc. Read More

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http://dx.doi.org/10.1109/TMI.2019.2903434DOI Listing
March 2019
1 Read

Probing surface-to-volume ratio of an anisotropic medium by diffusion NMR with general gradient encoding.

IEEE Trans Med Imaging 2019 Mar 5. Epub 2019 Mar 5.

Since the seminal paper by Mitra et al., diffusion MR has been widely used in order to estimate surfaceto-volume ratios. In the present work we generalize Mitra's formula for arbitrary diffusion encoding waveforms, including recently developed q-space trajectory encoding sequences. Read More

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http://dx.doi.org/10.1109/TMI.2019.2902957DOI Listing

Formulation and Efficient Computation of ℓ- and Smoothness Penalized Estimates for Microstructure-Informed Tractography.

IEEE Trans Med Imaging 2019 Mar 4. Epub 2019 Mar 4.

Fiber tractography based on diffusion-weighted magnetic resonance imaging is to date the only method for the three-dimensional visualization of nerve fiber bundles in the living human brain noninvasively. However, various existing methods suffer from reconstructing anatomically implausible fiber tracks due to exclusive local treatment of the input data. A method which seeks to filter out invalid tracks in a postprocessing step by solving a convex optimization problem with ℓ1-norm regularization was recently introduced in the work by Daducci et al. Read More

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http://dx.doi.org/10.1109/TMI.2019.2902787DOI Listing
March 2019
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High-Contrast, Low-Cost, 3D Visualization of Skin Cancer Using Ultra-High-Resolution Millimeter-Wave Imaging.

IEEE Trans Med Imaging 2019 Mar 4. Epub 2019 Mar 4.

The goal of this study is to develop a new skin imaging modality which addresses the current clinical need for a non-invasive imaging tool that images the skin over its depth with high resolutions while offering large histopathological-like contrasts between malignant and normal tissues. We demonstrate that by taking advantage of the intrinsic millimeter-wave dielectric contrasts between normal and malignant skin tissues, ultra-highresolution millimeter-wave imaging (UH-MMWI) can achieve three-dimensional, high-contrast images of the skin. In this work, an imaging system with a record-wide bandwidth of 98 GHz is developed using the synthetic ultra-wideband millimeter-wave imaging approach, a new ultra-high-resolution imaging technique recently developed by the authors. Read More

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http://dx.doi.org/10.1109/TMI.2019.2902600DOI Listing

Establishment of an Automated Algorithm Utilizing Optical Coherence Tomography and Micro-Computed Tomography Imaging to Reconstruct the 3-D Deformed Stent Geometry.

IEEE Trans Med Imaging 2019 Mar;38(3):710-720

Percutaneous coronary intervention (PCI) is the prevalent treatment for coronary artery disease, with hundreds of thousands of stents implanted annually. Computational studies have demonstrated the role of biomechanics in the failure of vascular stents, but clinical studies is this area are limited by a lack of understanding of the deployed stent geometry, which is required to accurately model and predict the stent-induced in vivo biomechanical environment. Herein, we present an automated method to reconstruct the 3-D deployed stent configuration through the fusion of optical coherence tomography (OCT) and micro-computed tomography ( μ CT) imaging data. Read More

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http://dx.doi.org/10.1109/TMI.2018.2870714DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407623PMC
March 2019
1 Read

Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks.

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e. Read More

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http://dx.doi.org/10.1109/TMI.2019.2902073DOI Listing
February 2019
6 Reads

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference.

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Read More

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http://dx.doi.org/10.1109/TMI.2019.2902044DOI Listing
February 2019
2 Reads

Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet.

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness. Convolutional neural networks (CNNs) are a powerful method to automatically learn the discriminative features for various tasks, including cancer detection. Read More

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http://dx.doi.org/10.1109/TMI.2019.2901928DOI Listing
February 2019

A Global Method for Non-Rigid Registration of Cell Nuclei in Live Cell Time-Lapse Images.

Authors:
Qi Gao Karl Rohr

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. Read More

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https://ieeexplore.ieee.org/document/8653983/
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http://dx.doi.org/10.1109/TMI.2019.2901918DOI Listing
February 2019
4 Reads

Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks.

IEEE Trans Med Imaging 2019 Feb 26. Epub 2019 Feb 26.

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may prohibit acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. Read More

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http://dx.doi.org/10.1109/TMI.2019.2901750DOI Listing
February 2019

Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. Read More

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https://ieeexplore.ieee.org/document/8654000/
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http://dx.doi.org/10.1109/TMI.2019.2901712DOI Listing
February 2019
15 Reads
3.390 Impact Factor

RETOUCH -The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

IEEE Trans Med Imaging 2019 Feb 26. Epub 2019 Feb 26.

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging and many retinal OCT analysis methods have been proposed. Read More

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https://ieeexplore.ieee.org/document/8653407/
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http://dx.doi.org/10.1109/TMI.2019.2901398DOI Listing
February 2019
7 Reads
3.390 Impact Factor

Monte Carlo simulations of water exchange through myelin wraps: Implications for diffusion MRI.

IEEE Trans Med Imaging 2019 Feb 27. Epub 2019 Feb 27.

Diffusion MRI yields parameters sensitive to brain tissue microstructure. A structurally important aspect of this microstructure is the myelin wrapping around the axons. This study investigated the forward problem concerning whether water exchange via the spiraling structure of the myelin can meaningfully contribute to the signal in dMRI. Read More

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http://dx.doi.org/10.1109/TMI.2019.2894398DOI Listing
February 2019
3 Reads

A Machine Learning Approach for Classifying Ischemic Stroke Onset Time from Imaging.

IEEE Trans Med Imaging 2019 Feb 25. Epub 2019 Feb 25.

Current clinical practice relies on clinical history to determine the time since stroke onset (TSS). Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options such as thrombolysis. Patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. Read More

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http://dx.doi.org/10.1109/TMI.2019.2901445DOI Listing
February 2019

The Cortical Network of Emotion Regulation: Insights from Advanced EEG-fMRI Integration Analysis.

IEEE Trans Med Imaging 2019 Feb 22. Epub 2019 Feb 22.

The ability to perceive and regulate emotion is a key component of cognition that is often disrupted by disease. Current neuroimaging studies regarding emotion regulation have implicated a number of cortical regions and identified several EEG features of interest, including the late positive potential (LPP) and frontal asymmetry. Unfortunately, currently applied methods generally lack in the resolution necessary to capture focal cortical activity and explore the causal interactions between brain regions. Read More

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http://dx.doi.org/10.1109/TMI.2019.2900978DOI Listing
February 2019
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Hand-held photoacoustic imager for theranostics in 3D.

IEEE Trans Med Imaging 2019 Feb 22. Epub 2019 Feb 22.

A handheld approach to three-dimensional (3D) photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm×64 mm×40 mm. Read More

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http://dx.doi.org/10.1109/TMI.2019.2900656DOI Listing
February 2019
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