# IEEE Transactions on Medical Imaging

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• ### The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Publication Year: 2015, Page(s):1993 - 2024
Cited by:  Papers (347)
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In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans ge... View full abstract»

• ### Image Reconstruction is a New Frontier of Machine Learning

Publication Year: 2018, Page(s):1289 - 1296
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Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” Thi... View full abstract»

• ### Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

Publication Year: 2018, Page(s):1562 - 1573
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Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address the... View full abstract»

• ### DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Publication Year: 2018, Page(s):1310 - 1321
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Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple ... View full abstract»

• ### Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

Publication Year: 2018, Page(s):384 - 395
Cited by:  Papers (4)
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Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promisin... View full abstract»

• ### Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Publication Year: 2016, Page(s):1207 - 1216
Cited by:  Papers (135)
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Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied... View full abstract»

• ### Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Publication Year: 2016, Page(s):1299 - 1312
Cited by:  Papers (212)
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Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images... View full abstract»

• ### Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Publication Year: 2016, Page(s):1240 - 1251
Cited by:  Papers (201)
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Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reaso... View full abstract»

• ### Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Publication Year: 2016, Page(s):1285 - 1298
Cited by:  Papers (370)
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Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a chal... View full abstract»

• ### AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

Publication Year: 2016, Page(s):1313 - 1321
Cited by:  Papers (70)
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The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotat... View full abstract»

• ### A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Publication Year: 2018, Page(s):491 - 503
Cited by:  Papers (1)
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Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that... View full abstract»

• ### Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound

Publication Year: 2018, Page(s): 1
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Volumetric ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. Biometrics obtained from the volumetric segmentation shed light on reformation of precise maternal and fetal health monitoring. However, the poor image quality, low contrast, boundary ambiguity and complex anatomy shapes conspire towards a great lack of efficient tools for the segmentation. It... View full abstract»

• ### CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction

Publication Year: 2018, Page(s):1440 - 1453
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We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism... View full abstract»

• ### 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

Publication Year: 2018, Page(s):1737 - 1750
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Limited capture range, and the requirement to provide high quality initialization for optimization-based 2-D/3-D image registration methods, can significantly degrade the performance of 3-D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion, such as fetal in-utero imaging, complicate the 3-D image and volume reco... View full abstract»

• ### Generative Adversarial Networks for Noise Reduction in Low-Dose CT

Publication Year: 2017, Page(s):2536 - 2545
Cited by:  Papers (15)
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Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained... View full abstract»

• ### Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

Publication Year: 2018, Page(s):1348 - 1357
Cited by:  Papers (3)
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The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists' judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the dia... View full abstract»

• ### Apparent Ultra-High$b$-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields

Publication Year: 2015, Page(s):1111 - 1124
Cited by:  Papers (7)
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A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b-values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate th... View full abstract»

• ### Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks

Publication Year: 2018, Page(s):1822 - 1834
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Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher a... View full abstract»

• ### Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning

Publication Year: 2018, Page(s):1464 - 1477
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Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propos... View full abstract»

• ### Segmenting Retinal Blood Vessels With Deep Neural Networks

Publication Year: 2016, Page(s):2369 - 2380
Cited by:  Papers (93)
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The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propo... View full abstract»

• ### Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

Publication Year: 2016, Page(s):1160 - 1169
Cited by:  Papers (118)  |  Patents (1)
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We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D p... View full abstract»

• ### Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Publication Year: 2016, Page(s):1252 - 1261
Cited by:  Papers (106)
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Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses ... View full abstract»

• ### Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Publication Year: 2016, Page(s):1182 - 1195
Cited by:  Papers (79)
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Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs ... View full abstract»

• ### Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors

Publication Year: 2018, Page(s):977 - 987
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Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-w... View full abstract»

• ### Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Publication Year: 2001, Page(s):45 - 57
Cited by:  Papers (2646)  |  Patents (6)
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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-de... View full abstract»

• ### Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography

Publication Year: 2018, Page(s):1382 - 1393
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Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates g... View full abstract»

• ### A CNN Regression Approach for Real-Time 2D/3D Registration

Publication Year: 2016, Page(s):1352 - 1363
Cited by:  Papers (43)
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In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the re... View full abstract»

• ### End-to-End Adversarial Retinal Image Synthesis

Publication Year: 2018, Page(s):781 - 791
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In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a lo... View full abstract»

• ### Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss

Publication Year: 2018, Page(s):1488 - 1497
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Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and im... View full abstract»

• ### Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

Publication Year: 2017, Page(s):1876 - 1886
Cited by:  Papers (1)
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Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural network... View full abstract»

• ### Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors

Publication Year: 2018, Page(s):1574 - 1586
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Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been shown to build a vector basis far more adapted to the discrimination of tissue and blood flow than the classical Fourier basis, improving by large factor clutter filtering and blood flow estimation. However, the question of optimally estimating the boundary between the tissue subspace and the blood flow subspa... View full abstract»

• ### Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems

Publication Year: 2018, Page(s):1454 - 1463
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In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forwa... View full abstract»

• ### Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI

Publication Year: 2018, Page(s): 1
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Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require trade offs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adver... View full abstract»

• ### Connectivity in fMRI: Blind Spots and Breakthroughs

Publication Year: 2018, Page(s):1537 - 1550
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In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of ... View full abstract»

• ### Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Publication Year: 2016, Page(s):1962 - 1971
Cited by:  Papers (21)
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Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists... View full abstract»

• ### 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

Publication Year: 2018, Page(s):1522 - 1534
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Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoisin... View full abstract»

• ### Learned Primal-Dual Reconstruction

Publication Year: 2018, Page(s):1322 - 1332
Cited by:  Papers (4)
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We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it d... View full abstract»

• ### Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing

Publication Year: 2016, Page(s):1217 - 1228
Cited by:  Papers (31)  |  Patents (1)
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Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be... View full abstract»

• ### Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Publication Year: 2016, Page(s):1196 - 1206
Cited by:  Papers (119)
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Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN)... View full abstract»

• ### Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

Publication Year: 2018, Page(s):1358 - 1369
Cited by:  Papers (1)
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Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a n... View full abstract»

• ### Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity

Publication Year: 2015, Page(s):2271 - 2285
Cited by:  Papers (92)
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Ultrafast ultrasonic imaging is a rapidly developing field based on the unfocused transmission of plane or diverging ultrasound waves. This recent approach to ultrasound imaging leads to a large increase in raw ultrasound data available per acquisition. Bigger synchronous ultrasound imaging datasets can be exploited in order to strongly improve the discrimination between tissue and blood motion in... View full abstract»

• ### Learning-Based Compressive MRI

Publication Year: 2018, Page(s):1394 - 1406
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In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework f... View full abstract»

• ### Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

Publication Year: 2018, Page(s):1418 - 1429
Cited by:  Papers (1)
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X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive p... View full abstract»

• ### Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Publication Year: 2017, Page(s):994 - 1004
Cited by:  Papers (41)
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Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very d... View full abstract»

• ### Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs

Publication Year: 2018, Page(s):1865 - 1876
| | PDF (2903 KB) | HTML

The success of deep convolutional neural networks (NNs) on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper, we investigate and propose NN architectures for automated multiclass segmentation of anatomical organs in chest radiographs (CXRs), namely for lungs, clavicles, and heart. We address se... View full abstract»

• ### Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

Publication Year: 2016, Page(s):1273 - 1284
Cited by:  Papers (59)
| | PDF (3058 KB) | HTML

Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute litt... View full abstract»

• ### Modeling Task fMRI Data Via Deep Convolutional Autoencoder

Publication Year: 2018, Page(s):1551 - 1561
| | PDF (3025 KB) | HTML Media

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches ha... View full abstract»

• ### Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography

Publication Year: 2018, Page(s):1370 - 1381
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In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the o... View full abstract»

• ### A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

Publication Year: 2018, Page(s):1407 - 1417
| | PDF (4349 KB) | HTML Media

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained ... View full abstract»

• ### Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

Publication Year: 2018, Page(s):1835 - 1846
| | PDF (2691 KB) | HTML

Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a new fully automated deep learning framework for EAT and thoracic adipose tissue (TAT) quantification from non-contrast coronary artery calcium computed ... View full abstract»

## Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structure, morphology and function, including cell and molecular imaging and all forms of microscopy. The journal publishes original contributions on medical imaging achieved by modalities including ultrasound, x-rays, magnetic resonance, radionuclides, microwaves, and optical methods. Contributions describing novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and related methods are encouraged. Studies involving highly technical perspectives are most welcome.

The focus of the journal is on unifying the sciences of medicine, biology, and imaging. It emphasizes the common ground where instrumentation, hardware, software, mathematics, physics, biology, and medicine interact through new analysis methods. Strong application papers that describe novel methods are particularly encouraged. Papers describing important applications based on medically adopted and/or established methods without significant innovation in methodology will be directed to other journals.

To qualify for publication, submitted manuscripts must be previously unpublished and must not be under consideration elsewhere. The Editor-in-Chief and an Associate Editor will perform a quick review of each manuscript to evaluate the manuscript in terms of novelty, quality and appropriateness and may return the manuscript immediately if it does not meet minimum standards of quality, originality, and scope. Manuscripts will ONLY be accepted in electronic format through ScholarOne Manuscripts. Please go to the ScholarOne Manuscripts website at http://mc.manuscriptcentral.com/tmi-ieee or to the TMI website http://www.ieee-tmi.org/ to find instructions to electronically submit your manuscript. Do not send original submissions or revisions directly to the Editor-in-Chief or Associate Editors.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Michael Insana
Beckman Institute for Advanced Science and Technology
Department of Bioengineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
m.f.i@ieee.org