Literature DB >> 29877510

Real-Time Medical Video Denoising with Deep Learning: Application to Angiography.

Praneeth Sadda1, Taha Qarni1.   

Abstract

This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.

Entities:  

Keywords:  angiography; deep learning; denoising; fluoroscopy; machine learning; medical imaging; neural network; real-time

Year:  2018        PMID: 29877510      PMCID: PMC5985814          DOI: 10.5120/ijais2018451755

Source DB:  PubMed          Journal:  Int J Appl Inf Syst        ISSN: 2249-085X


  13 in total

1.  A fast learning algorithm for deep belief nets.

Authors:  Geoffrey E Hinton; Simon Osindero; Yee-Whye Teh
Journal:  Neural Comput       Date:  2006-07       Impact factor: 2.026

2.  Reducing the dimensionality of data with neural networks.

Authors:  G E Hinton; R R Salakhutdinov
Journal:  Science       Date:  2006-07-28       Impact factor: 47.728

3.  Image denoising by sparse 3-D transform-domain collaborative filtering.

Authors:  Kostadin Dabov; Alessandro Foi; Vladimir Katkovnik; Karen Egiazarian
Journal:  IEEE Trans Image Process       Date:  2007-08       Impact factor: 10.856

Review 4.  Radial versus femoral access for coronary angiography or intervention and the impact on major bleeding and ischemic events: a systematic review and meta-analysis of randomized trials.

Authors:  Sanjit S Jolly; Shoaib Amlani; Martial Hamon; Salim Yusuf; Shamir R Mehta
Journal:  Am Heart J       Date:  2008-11-01       Impact factor: 4.749

5.  Wavelet based noise reduction in CT-images using correlation analysis.

Authors:  Anja Borsdorf; Rainer Raupach; Thomas Flohr; Joachim Hornegger
Journal:  IEEE Trans Med Imaging       Date:  2008-12       Impact factor: 10.048

Review 6.  Learning multiple layers of representation.

Authors:  Geoffrey E Hinton
Journal:  Trends Cogn Sci       Date:  2007-10       Impact factor: 20.229

7.  Noise in MRI.

Authors:  A Macovski
Journal:  Magn Reson Med       Date:  1996-09       Impact factor: 4.668

Review 8.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 9.  Angiographic success and procedural complications in patients undergoing percutaneous coronary chronic total occlusion interventions: a weighted meta-analysis of 18,061 patients from 65 studies.

Authors:  Vishal G Patel; Kimberly M Brayton; Aracely Tamayo; Owen Mogabgab; Tesfaldet T Michael; Nathan Lo; Mohammed Alomar; Deborah Shorrock; Daisha Cipher; Shuaib Abdullah; Subhash Banerjee; Emmanouil S Brilakis
Journal:  JACC Cardiovasc Interv       Date:  2013-01-23       Impact factor: 11.195

10.  Mitral Subvalvular Aneurysm in a Patient with Chagas Disease and Recurrent Episodes of Ventricular Tachycardia.

Authors:  Tereza Augusta Grillo; Guilherme Rafael S Athayde; Ana Flávia L Belfort; Reynaldo C Miranda; Andrea Z Beaton; Bruno R Nascimento
Journal:  Case Rep Cardiol       Date:  2015-11-08
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  2 in total

Review 1.  Applications of Deep Learning to Neuro-Imaging Techniques.

Authors:  Guangming Zhu; Bin Jiang; Liz Tong; Yuan Xie; Greg Zaharchuk; Max Wintermark
Journal:  Front Neurol       Date:  2019-08-14       Impact factor: 4.003

2.  Synthesis of fracture radiographs with deep neural networks.

Authors:  Nicholas Chedid; Praneeth Sadda; Anish Gonchigar; Jonathan Langdon; Jack Porrino; Andrew Haims; Richard Andrew Taylor
Journal:  Health Inf Sci Syst       Date:  2020-05-30
  2 in total

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