Deep learning in medical imaging and radiation therapy.
Berkman Sahiner,Aria Pezeshk,Lubomir M. Hadjiiski,Xiaosong Wang,Karen Drukker,Kenny H. Cha,Ronald M. Summers,Maryellen L. Giger +7 more
TL;DR: The general principles of DL and convolutional neural networks are introduced, five major areas of application of DL in medical imaging and radiation therapy are surveyed, common themes are identified, methods for dataset expansion are discussed, and lessons learned, remaining challenges, and future directions are summarized.
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Abstract: The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.
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Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.
TL;DR: It is shown that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data, which will be valuable to medical imaging researchers to reduce manual segmentation effort and cost in CT imaging.
Deep learning in medical image registration: a review.
TL;DR: A comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets is provided and the statistics of all the cited works from various aspects are analyzed, revealing the popularity and future trend ofDL-based medical image registration.
Deep learning in medical image registration: a survey
Grant Haskins,Uwe Kruger,Pingkun Yan +2 more
- 01 Feb 2020
TL;DR: This survey outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years and highlights future research directions to show how this field may be possibly moved forward to the next level.
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The 'Digital Twin' to enable the vision of precision cardiology.
Jorge Corral-Acero,Francesca Margara,Maciej Marciniak,Cristobal Rodero,Filip Loncaric,Yingjing Feng,Andrew Gilbert,Joao Filipe Fernandes,Hassaan A. Bukhari,Ali Wajdan,Manuel Villegas Martinez,Mariana Sousa Santos,Mehrdad Shamohammdi,Hongxing Luo,Philip Westphal,Paul Leeson,Paolo DiAchille,Viatcheslav Gurev,Manuel Mayr,Liesbet Geris,Pras Pathmanathan,Tina M. Morrison,Richard Cornelussen,Frits W. Prinzen,Tammo Delhaas,Ada Doltra,Marta Sitges,Edward J. Vigmond,Ernesto Zacur,Vicente Grau,Blanca Rodriguez,Espen W. Remme,Steven A. Niederer,Peter Mortier,Kristin McLeod,Mark Potse,Esther Pueyo,Alfonso Bueno-Orovio,Pablo Lamata +38 more
TL;DR: It is argued that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient.
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A gentle introduction to deep learning in medical image processing
TL;DR: A gentle introduction to deep learning in medical image processing is given, proceeding from theoretical foundations to applications, including general reasons for the popularity of deep learning, including several major breakthroughs in computer science.
References
Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example
Nancy A. Obuchowski,Huiman X. Barnhart,Andrew J. Buckler,Gene Pennello,Xiao-Feng Wang,Jayashree Kalpathy-Cramer,Hyun J. Kim,Anthony P. Reeves +7 more
TL;DR: This paper illustrates the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms in quantitative imaging biomarkers with data from three studies of pulmonary nodules.
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Dose Prediction with U-net: A Feasibility Study for Predicting Dose Distributions from Contours using Deep Learning on Prostate IMRT Patients.
Dan Nguyen,Troy Long,Xun Jia,Weiguo Lu,Xuejun Gu,Zohaib Iqbal,Steve B. Jiang +6 more
- 26 Sep 2017
TL;DR: A novel application of the fully convolutional deep network model, U-net, for predicting dose from patient contours is developed, able to accurately predict the dose of prostate cancer patients, where the average dice similarity coefficient is well over 0.9.
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Hayit Greenspan,Bram van Ginneken,Ronald M. Summers +2 more
- 29 Apr 2016
TL;DR: The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.
Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box
Francesco Ciompi,Bartjan de Hoop,Sarah J. van Riel,Kaman Chung,Ernst T. Scholten,Matthijs Oudkerk,Pim A. de Jong,Mathias Prokop,Bram van Ginneken +8 more
TL;DR: This paper compares its approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrates the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.
End-to-End Adversarial Retinal Image Synthesis
Pedro Costa,Adrian Galdran,Maria Ines Meyer,Meindert Niemeijer,Michael D. Abràmoff,Ana Maria Mendonça,Aurélio Campilho +6 more
TL;DR: This paper proposes to implement an adversarial autoencoder for the task of retinal vessel network synthesis, and uses the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network.
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