IEEE Transactions on Medical Imaging
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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To qualify for publication, submitted manuscripts must be previously unpublished and must not be under consideration elsewhere.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.
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze ; Andras Jakab ; Stefan Bauer ; Jayashree Kalpathy-Cramer ; Keyvan Farahani ; Justin Kirby ; Yuliya Burren ; Nicole Porz ; Johannes Slotboom ; Roland Wiest ; Levente Lanczi ; Elizabeth Gerstner ; Marc-André Weber ; Tal Arbel ; Brian B. Avants ; Nicholas Ayache ; Patricia Buendia ; D. Louis Collins ; Nicolas Cordier ; Jason J. Corso ; Antonio Criminisi ; Tilak Das ; Hervé Delingette ; Çağatay Demiralp ; Christopher R. Durst ; Michel Dojat ; Senan Doyle ; Joana Festa ; Florence Forbes ; Ezequiel Geremia ; Ben Glocker ; Polina Golland ; Xiaotao Guo ; Andac Hamamci ; Khan M. Iftekharuddin ; Raj Jena ; Nigel M. John ; Ender Konukoglu ; Danial Lashkari ; José António Mariz ; Raphael Meier ; Sérgio Pereira ; Doina Precup ; Stephen J. Price ; Tammy Riklin Raviv ; Syed M. S. Reza ; Michael Ryan ; Duygu Sarikaya ; Lawrence Schwartz ; Hoo-Chang Shin ; Jamie Shotton ; Carlos A. Silva ; Nuno Sousa ; Nagesh K. Subbanna ; Gabor Szekely ; Thomas J. Taylor ; Owen M. Thomas ; Nicholas J. Tustison ; Gozde Unal ; Flor Vasseur ; Max Wintermark ; Dong Hye Ye ; Liang Zhao ; Binsheng Zhao ; Darko Zikic ; Marcel Prastawa ; Mauricio Reyes ; Koen Van LeemputThu Dec 04 00:00:00 EST 2014 Thu Dec 04 00:00:00 EST 2014 -
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang ; Wenqi Li ; Maria A. Zuluaga ; Rosalind Pratt ; Premal A. Patel ; Michael Aertsen ; Tom Doel ; Anna L. David ; Jan Deprest ; Sébastien Ourselin ; Tom VercauterenFri Jan 26 00:00:00 EST 2018 Fri Jan 26 00:00:00 EST 2018 -
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Guang Yang ; Simiao Yu ; Hao Dong ; Greg Slabaugh ; Pier Luigi Dragotti ; Xujiong Ye ; Fangde Liu ; Simon Arridge ; Jennifer Keegan ; Yike Guo ; David FirminThu Dec 21 00:00:00 EST 2017 Thu Dec 21 00:00:00 EST 2017 -
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Fri Mar 04 00:00:00 EST 2016 Fri Mar 04 00:00:00 EST 2016 -
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay ; Enzo Ferrante ; Konstantinos Kamnitsas ; Mattias Heinrich ; Wenjia Bai ; Jose Caballero ; Stuart A. Cook ; Antonio de Marvao ; Timothy Dawes ; Declan P. O‘Regan ; Bernhard Kainz ; Ben Glocker ; Daniel RueckertTue Sep 26 00:00:00 EDT 2017 Tue Sep 26 00:00:00 EDT 2017
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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
Popular Documents (January 2019)
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze ; Andras Jakab ; Stefan Bauer ; Jayashree Kalpathy-Cramer ; Keyvan Farahani ; Justin Kirby ; Yuliya Burren ; Nicole Porz ; Johannes Slotboom ; Roland Wiest ; Levente Lanczi ; Elizabeth Gerstner ; Marc-André Weber ; Tal Arbel ; Brian B. Avants ; Nicholas Ayache ; Patricia Buendia ; D. Louis Collins ; Nicolas Cordier ; Jason J. Corso ; Antonio Criminisi ; Tilak Das ; Hervé Delingette ; Çağatay Demiralp ; Christopher R. Durst ; Michel Dojat ; Senan Doyle ; Joana Festa ; Florence Forbes ; Ezequiel Geremia ; Ben Glocker ; Polina Golland ; Xiaotao Guo ; Andac Hamamci ; Khan M. Iftekharuddin ; Raj Jena ; Nigel M. John ; Ender Konukoglu ; Danial Lashkari ; José António Mariz ; Raphael Meier ; Sérgio Pereira ; Doina Precup ; Stephen J. Price ; Tammy Riklin Raviv ; Syed M. S. Reza ; Michael Ryan ; Duygu Sarikaya ; Lawrence Schwartz ; Hoo-Chang Shin ; Jamie Shotton ; Carlos A. Silva ; Nuno Sousa ; Nagesh K. Subbanna ; Gabor Szekely ; Thomas J. Taylor ; Owen M. Thomas ; Nicholas J. Tustison ; Gozde Unal ; Flor Vasseur ; Max Wintermark ; Dong Hye Ye ; Liang Zhao ; Binsheng Zhao ; Darko Zikic ; Marcel Prastawa ; Mauricio Reyes ; Koen Van LeemputPublication Year: 2015, Page(s):1993 - 2024
Cited by: Papers (475)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»
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Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang ; Wenqi Li ; Maria A. Zuluaga ; Rosalind Pratt ; Premal A. Patel ; Michael Aertsen ; Tom Doel ; Anna L. David ; Jan Deprest ; Sébastien Ourselin ; Tom VercauterenPublication Year: 2018, Page(s):1562 - 1573
Cited by: Papers (1)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»
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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Guang Yang ; Simiao Yu ; Hao Dong ; Greg Slabaugh ; Pier Luigi Dragotti ; Xujiong Ye ; Fangde Liu ; Simon Arridge ; Jennifer Keegan ; Yike Guo ; David FirminPublication Year: 2018, Page(s):1310 - 1321
Cited by: Papers (3)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»
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Publication Year: 2016, Page(s):1240 - 1251
Cited by: Papers (267)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»
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Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay ; Enzo Ferrante ; Konstantinos Kamnitsas ; Mattias Heinrich ; Wenjia Bai ; Jose Caballero ; Stuart A. Cook ; Antonio de Marvao ; Timothy Dawes ; Declan P. O‘Regan ; Bernhard Kainz ; Ben Glocker ; Daniel RueckertPublication Year: 2018, Page(s):384 - 395
Cited by: Papers (8)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»
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Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
Marios Anthimopoulos ; Stergios Christodoulidis ; Lukas Ebner ; Andreas Christe ; Stavroula MougiakakouPublication Year: 2016, Page(s):1207 - 1216
Cited by: Papers (166)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»
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A Parametric Level Set-Based Approach to Difference Imaging in Electrical Impedance Tomography
Publication Year: 2019, Page(s):145 - 155
Cited by: Papers (1)This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key ... View full abstract»
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Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh ; Jae Y. Shin ; Suryakanth R. Gurudu ; R. Todd Hurst ; Christopher B. Kendall ; Michael B. Gotway ; Jianming LiangPublication Year: 2016, Page(s):1299 - 1312
Cited by: Papers (308)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»
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Image Reconstruction is a New Frontier of Machine Learning
Publication Year: 2018, Page(s):1289 - 1296
Cited by: Papers (1)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»
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Deep Generative Adversarial Neural Networks for Compressive Sensing MRI
Morteza Mardani ; Enhao Gong ; Joseph Y. Cheng ; Shreyas S. Vasanawala ; Greg Zaharchuk ; Lei Xing ; John M. PaulyPublication Year: 2019, Page(s):167 - 179Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs 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 advers... View full abstract»
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin ; Holger R. Roth ; Mingchen Gao ; Le Lu ; Ziyue Xu ; Isabella Nogues ; Jianhua Yao ; Daniel Mollura ; Ronald M. SummersPublication Year: 2016, Page(s):1285 - 1298
Cited by: Papers (505)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»
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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Publication Year: 2018, Page(s):491 - 503
Cited by: Papers (6)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»
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AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Shadi Albarqouni ; Christoph Baur ; Felix Achilles ; Vasileios Belagiannis ; Stefanie Demirci ; Nassir NavabPublication Year: 2016, Page(s):1313 - 1321
Cited by: Papers (93)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»
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3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images
Benjamin Hou ; Bishesh Khanal ; Amir Alansary ; Steven McDonagh ; Alice Davidson ; Mary Rutherford ; Jo V. Hajnal ; Daniel Rueckert ; Ben Glocker ; Bernhard KainzPublication Year: 2018, Page(s):1737 - 1750
Cited by: Papers (1)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»
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CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
Publication Year: 2018, Page(s):1440 - 1453We 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»
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Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis
Publication Year: 2019, Page(s):46 - 56We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated data sets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic fundus image data set using the proposed R-sGAN technique. The resulting synthetic images are realistic-looking in terms of the query images while ma... View full abstract»
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Generative Adversarial Networks for Noise Reduction in Low-Dose CT
Publication Year: 2017, Page(s):2536 - 2545
Cited by: Papers (27)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»
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Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images
Russell Bates ; Benjamin Irving ; Bostjan Markelc ; Jakob Kaeppler ; Graham Brown ; Ruth J. Muschel ; Sir Michael Brady ; Vicente Grau ; Julia A. SchnabelPublication Year: 2019, Page(s):1 - 10Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visual... View full abstract»
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A CNN Regression Approach for Real-Time 2D/3D Registration
Publication Year: 2016, Page(s):1352 - 1363
Cited by: Papers (61)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»
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Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
Qingsong Yang ; Pingkun Yan ; Yanbo Zhang ; Hengyong Yu ; Yongyi Shi ; Xuanqin Mou ; Mannudeep K. Kalra ; Yi Zhang ; Ling Sun ; Ge WangPublication Year: 2018, Page(s):1348 - 1357
Cited by: Papers (5)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»
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard ; Alain Lalande ; Clement Zotti ; Frederick Cervenansky ; Xin Yang ; Pheng-Ann Heng ; Irem Cetin ; Karim Lekadir ; Oscar Camara ; Miguel Angel Gonzalez Ballester ; Gerard Sanroma ; Sandy Napel ; Steffen Petersen ; Georgios Tziritas ; Elias Grinias ; Mahendra Khened ; Varghese Alex Kollerathu ; Ganapathy Krishnamurthi ; Marc-Michel Rohé ; Xavier Pennec ; Maxime Sermesant ; Fabian Isensee ; Paul Jäger ; Klaus H. Maier-Hein ; Peter M. Full ; Ivo Wolf ; Sandy Engelhardt ; Christian F. Baumgartner ; Lisa M. Koch ; Jelmer M. Wolterink ; Ivana Išgum ; Yeonggul Jang ; Yoonmi Hong ; Jay Patravali ; Shubham Jain ; Olivier Humbert ; Pierre-Marc JodoinPublication Year: 2018, Page(s):2514 - 2525
Cited by: Papers (1)Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large... View full abstract»
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Apparent Ultra-High
-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields$b$ Mohammad Javad Shafiee ; Shahid A. Haider ; Alexander Wong ; Dorothy Lui ; Andrew Cameron ; Ameen Modhafar ; Paul Fieguth ; Masoom A. HaiderPublication Year: 2015, Page(s):1111 - 1124
Cited by: Papers (7)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»
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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Publication Year: 2018, Page(s):2663 - 2674
Cited by: Papers (1)Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-... View full abstract»
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Segmenting Retinal Blood Vessels With Deep Neural Networks
Publication Year: 2016, Page(s):2369 - 2380
Cited by: Papers (122)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»
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Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
Arnaud Arindra Adiyoso Setio ; Francesco Ciompi ; Geert Litjens ; Paul Gerke ; Colin Jacobs ; Sarah J. van Riel ; Mathilde Marie Winkler Wille ; Matiullah Naqibullah ; Clara I. Sánchez ; Bram van GinnekenPublication Year: 2016, Page(s):1160 - 1169
Cited by: Papers (164) | Patents (1)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»
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.
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
Further Links
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.
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Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=42 More »
Frequency: 12
ISSN: 0278-0062
Publication Details:
Publication Details:
Editorial Board:
Subjects
- Bioengineering
- Computing & Processing
Contacts
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
Editorial Office
Deborah Insana
mfi.tmi@gmail.com
About this Journal
Author Resources
Sponsor

Contacts
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