IEEE Transactions on Medical Imaging information for authors
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Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

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Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Introducing IEEE Collabratec
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Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Towards Contactless Patient Positioning
The ongoing COVID-19 pandemic, caused by the highly contagious SARS-CoV-2 virus, has overwhelmed healthcare systems worldwide, putting medical professionals at a high risk of getting infected themselves due to a global shortage of personal protective equipment. This has in-turn led to understaffed hospitals unable to handle new patient influx. To help alleviate these problems, we design and develop a contactless patient positioning system that can enable scanning patients in a completely remote and contactless fashion. Our key design objective is to reduce the physical contact time with a patient as much as possible, which...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimen...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduc...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by disease...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images
Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noi...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intellige...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combinin...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning
In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a l...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lu...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning
Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COV...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images
We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily buil...
Source: IEE Transactions on Medical Imaging - July 31, 2020 Category: Biomedical Engineering Source Type: research

Guest Editorial: Special Issue on Imaging-Based Diagnosis of COVID-19
The novel coronavirus 2019 (COVID-19) began infecting humans in late 2019 and then turned into pandemic in the successive months spreading all over the world. At the beginning of July 2020, the global number of confirmed cases reported by the World Health Organization is above 10 million, with more than half million deaths and a rate of new cases of almost 150 000 per day. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 31, 2020 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 - July 31, 2020 Category: Biomedical Engineering Source Type: research

Table of contents
(Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 31, 2020 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 - July 1, 2020 Category: Biomedical Engineering Source Type: research

Introducing IEEE Collabratec
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Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Correction to “Noise Adaptation Generative Adversarial Network for Medical Image Analysis”
In the above article [1], Tables II, III, and V and Fig. 6 are incorrect. The correct images are provided below: (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the well-established pix2pix network (image-to-image network) is modified to fit the need for multitaskin...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis
Brain network provides essential insights in diagnosing many brain disorders. Integrative analysis of multiple types of connectivity, e.g, functional connectivity (FC) and structural connectivity (SC), can take advantage of their complementary information and therefore may help to identify patients. However, traditional brain network methods usually focus on either FC or SC for describing node interactions and only consider the interaction between paired network nodes. To tackle this problem, in this paper, we propose an Attention-Diffusion-Bilinear Neural Network (ADB-NN) framework for brain network analysis, which is tra...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. I...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Motion Dependent and Spatially Variant Resolution Modeling for PET Rigid Motion Correction
Recent advances in positron emission tomography (PET) have allowed to perform brain scans of freely moving animals by using rigid motion correction. One of the current challenges in these scans is that, due to the PET scanner spatially variant point spread function (SVPSF), motion corrected images have a motion dependent blurring since animals can move throughout the entire field of view (FOV). We developed a method to calculate the image-based resolution kernels of the motion dependent and spatially variant PSF (MD-SVPSF) to correct the loss of spatial resolution in motion corrected reconstructions. The resolution kernels...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to a 3D dataset ...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain. Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and en...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On o...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain
We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Recalibrating 3D ConvNets With Project & Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks termed squeeze and excitation (SE) have been introduced to recalibrate F-CNN feature maps both channel- and spatial-wise, boosting segmentation performance while only minimally increasing the model complexity. So far, the development of SE blocks has focused on 2D architectures. For volumetric medical images, however, 3D F-CNNs are a natural choice. In this article, we extend existing 2D recalibration methods to 3D and propose a generic compress-proc...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Model Comparison Metrics Require Adaptive Correction if Parameters Are Discretized: Proof-of-Concept Applied to Transient Signals in Dynamic PET
Linear parametric neurotransmitter PET (lp-ntPET) is a novel kinetic model that estimates the temporal characteristics of a transient neurotransmitter component in PET data. To preserve computational simplicity in estimation, the parameters of the nonlinear term that describe this transient signal are discretized, and only a limited set of values for each parameter are allowed. Thus, linear estimation can be performed. Linear estimation is implemented using predefined basis functions that incorporate the discretized parameters. The implementation of the model using discretized parameters poses unique challenges for signifi...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners
Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth position rearrangements in orthodontic treatment planning. However, it is a challenging task primarily due to the abnormal and varying appearance of patients’ teeth. The emerging utilization of intraoral scanners (IOSs) in clinics further increases the difficulty in automated tooth labeling, as the raw surfaces acquired by IOS are typically low-quality at gingival and deep intraoral regions. In recent years, some pioneering end-to-end methods (e.g., PointNet) have been proposed in the communities of computer vision and grap...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Preclinical Imaging Using Single Track Location Shear Wave Elastography: Monitoring the Progression of Murine Pancreatic Tumor Liver Metastasis In Vivo
Recently, researchers have discovered the direct impact of the tumor mechanical environment on the growth, drug uptake and prognosis of tumors. While estimating the mechanical parameters (solid stress, fluid pressure, stiffness) can aid in the treatment planning and monitoring, most of these parameters cannot be quantified noninvasively. Shear wave elastography (SWE) has shown promise as a means of noninvasively measuring the stiffness of soft tissue. However, stiffness is still not a recognized imaging biomarker. While SWE has been shown to be capable of measuring tumor stiffness in humans, much important research is done...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Unpaired Multi-Modal Segmentation via Knowledge Distillation
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss ter...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Ultra-Compact Microsystems-Based Confocal Endomicroscope
Point-of-care medical diagnosis demands immediate feedback on tissue pathology. Confocal endomicroscopy can provide real-time in vivo images with histology-like features. The working channel in medical endoscopes are becoming smaller in dimension. Microsystems methods can produce tiny mechanical scanners. We demonstrate a flexible fiber instrument for in vivo imaging as an endoscope accessory. The optical path is folded on-axis to reduce length while allowing the beam to expand and achieve a numerical aperture of 0.41. A high-speed parametric resonance mirror produces large deflection angles> 13°, and is mounted on ...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. $224times 224$ ) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of $1792times 1792$ pixels. The proposed framework first...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse, mainly related to the complexity in manual annotation. Deep convolutional neural networks (CNNs), with transferable knowledge, have been employed as a solution to limited annotated data through: 1) fine-tuning generic knowledge with a relatively smaller amount of labelled medical imaging data, and 2) learning image representation that is invariant to different domains. These approaches, however, are still reli...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Deep Neural Networks for Chronological Age Estimation From OPG Images
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are propose...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Novel Regional Activity Representation With Constrained Canonical Correlation Analysis for Brain Connectivity Network Estimation
Inferring brain connectivity networks from fMRI data can take place at the Region of Interest (ROI) or voxel level. With most ROI–based approaches, the signals from same-ROI voxels are simply averaged, neglecting any inhomogeneity in each ROI and assuming that the same voxels will interact with different ROIs in a similar manner. In this paper, we propose a novel method of representing ROI activity and estimating brain connectivity that takes into account the regionally-specific nature of brain activity, the spatial location of concentrated activity, and activity in other ROIs. The proposed method is able to integrat...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation
This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provi...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis
Generative adversarial network (GAN) has been widely explored for cross-modality medical image synthesis. The existing GAN models usually adversarially learn a global sample space mapping from the source-modality to the target-modality and then indiscriminately apply this mapping to all samples in the whole space for prediction. However, due to the scarcity of training samples in contrast to the complicated nature of medical image synthesis, learning a single global sample space mapping that is “optimal” to all samples is very challenging, if not intractable. To address this issue, this paper proposes sample-ad...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

A Robust Regularizer for Multiphase CT
Joint image reconstruction for multiphase CT can potentially improve image quality and reduce dose by leveraging the shared information among the phases. Multiphase CT scans are acquired sequentially. Inter-scan patient breathing causes small organ shifts and organ boundary misalignment among different phases. Existing multi-channel regularizers such as the joint total variation (TV) can introduce artifacts at misaligned organ boundaries. We propose a multi-channel regularizer using the infimal convolution (inf-conv) between a joint TV and a separable TV. It is robust against organ misalignment; it can work like a joint TV...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps
Multi-label probabilistic maps, a.k.a. probabilistic segmentations, parameterize a population of intimately co-existing anatomical shapes and are useful for various medical imaging applications, such as segmentation, anatomical atlases, shape analysis, and consensus generation. Existing methods to estimate probabilistic segmentations rely on ad hoc intermediate representations (e.g., average of Gaussian-smoothed label maps and smoothed signed distance maps) that do not necessarily conform to the underlying generative process. Generative modeling of such maps could help discover as well as aide in the statistical analysis o...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning
Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided brachytherapy. However, most current studies are concentrated on single-needle detection by only using a small number of images with a needle, regardless of the massive database of US images without needles. In this paper, we propose a workflow for multi-needle detection by considering the images without needles as auxiliary. Concretely, we train position-specific dictionaries on 3D overlapping patches of auxiliary images, where we develop an enhanced sparse dictionary learning method by...
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network
Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image quality of low-dose CT (LDCT) to reduce the cumulative radiation exposure of patients undergoing routine screening exams. The resurgence of deep learning has yielded a new approach for noise reduction by training a deep multi-layer convolutional neural networks (CNN) to map the low-dose to normal-dose CT images. However, CNN-based methods heavily rely on convolutional kernels, wh...
Source: IEE Transactions on Medical Imaging - July 1, 2020 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 - July 1, 2020 Category: Biomedical Engineering Source Type: research

Table of contents
Presents the table of contents for this issue of the publication. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - July 1, 2020 Category: Biomedical Engineering Source Type: research

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy...
Source: IEE Transactions on Medical Imaging - June 1, 2020 Category: Biomedical Engineering Source Type: research

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