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Medical image segmentation is a difficult but important task for many clinical operations such as cardiac bi-ventricular volume estimation.
Snakes: Active contour models
Michael Kass, Andrew P. Witkin, and Demetri Terzopoulos · 1988
Earlier work this paper cites.
Coronary atherosclerosis and its effect on cardiac structure and function: evaluation by electron beam computed tomography
William Stanford and Brad H Thompson · 1998
Earlier work this paper cites.
St-segment deviation analysis of the admission 12-lead electrocardiogram as an aid to early diagnosis of acute myocardial infarction with a cardiac magnetic resonance imaging gold standard
Thomas N Martin, Bjoern A Groenning, Heather M Murray, Tracey Steedman, John E Foster, Alex T Elliot, Henry J Dargie, Ronald H Selvester, Olle Pahlm, and Galen S Wagner · 2007
Earlier work this paper cites.
Noninvasive assessment of myocardial viability in a small animal model: comparison of mri, spect, and pet
Daniel Thomas, Harshali Bal, Jeffrey Arkles, James Horowitz, Luis Araujo, Paul D Acton, and Victor A Ferrari · 2008
Earlier work this paper cites.
Understanding cardiac output
Jean-Louis Vincent · 2008
Earlier work this paper cites.
Evaluation framework for algorithms segmenting short axis cardiac mri
P. Radau, Y. Lu, K. Connelly, G. Paul, A. Dick, and G. Wright · 2009
Earlier work this paper cites.
Cardiac mri: a new gold standard for ventricular volume quantification during high-intensity exercise
A Gerche La, Guido Claessen, A de Bruaene Van, Nele Pattyn, J Cleemput Van, Marc Gewillig, Jan Bogaert, Steven Dymarkowski, Piet Claus, and Hein Heidbuchel · 2013
Earlier work this paper cites.
Quantification of left ventricular indices from ssfp cine imaging: Impact of real‐world variability in analysis methodology and utility of geometric modeling
Christopher A Miller, Peter Jordan, Alex Borg, Rachel Argyle, David Clark, Keith Pearce, and Matthias Schmitt · 2013
Earlier work this paper cites.
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri, 2015
M. R. Avendi, A. Kheradvar, and H. Jafarkhani · 2015
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique
Hayit Greenspan, Bram Van Ginneken, and Ronald M Summers · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten · 2016
Earlier work this paper cites.
The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, 2016
Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization, 2016
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2016
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A fully convolutional neural network for cardiac segmentation in short-axis mri, 2016
Phi Vu Tran · 2016
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Aggregated residual transformations for deep neural networks, 2016
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Automatic myocardial segmentation by using a deep learning network in cardiac mri, 2017
Ariel H. Curiale, Flavio D. Colavecchia, Pablo Kaluza, Roberto A. Isoardi, and German Mato · 2017
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness, 2018
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2018
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Machine learning in medical imaging
Maryellen L Giger · 2018
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Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing
Grinias Ilias and Georgios Tziritas · 2018
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Automatic segmentation of lv and rv in cardiac mri
Yeonggul Jang, Yoonmi Hong, Seongmin Ha, Sekeun Kim, and Hyuk-Jae Chang · 2018
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Saumya Jetley, Nicholas A Lord, Namhoon Lee, and Philip HS Torr · 2018
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Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu · 2017
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The (un)reliability of saliency methods, 2017
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2017
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Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks
Liset Vázquez Romaguera, Marly Guimarães Fernandes Costa, Francisco Perdigón Romero, and Cicero Ferreira Fernandes Costa Filho · 2017
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Smoothgrad: removing noise by adding noise, 2017
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images
X. Yang, Z. Zeng, and S. Yi · 2017
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Shape-aware deep convolutional neural network for vertebrae segmentation
S. M. Masudur Rahman Al Arif, Karen Knapp, and Greg Slabaugh · 2018
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Spnet: Shape prediction using a fully convolutional neural network
S. M. Masudur Rahman Al Arif, Karen Knapp, and Greg Slabaugh · 2018
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Densely connected fully convolutional network for short-axis cardiac cine mr image segmentation and heart diagnosis using random forest
Mahendra Khened, Varghese Alex, and Ganapathy Krishnamurthi · 2018
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2d-3d fully convolutional neural networks for cardiac mr segmentation
Jay Patravali, Shubham Jain, and Sasank Chilamkurthy · 2018
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Attention gated networks: Learning to leverage salient regions in medical images, 2018
Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, and Daniel Rueckert · 2018
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Automatic segmentation and disease classification using cardiac cine mr images
Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, and Ivana Išgum · 2018
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Learning active contour models for medical image segmentation
Xu Chen, Bryan M. Williams, Srinivasa R. Vallabhaneni, Gabriela Czanner, Rachel Williams, and Yalin Zheng · 2019
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Giana polyp segmentation with fully convolutional dilation neural networks
Y. B. Guo and Bogdan J. Matuszewski · 2019
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On the variance of the adaptive learning rate and beyond, 2019
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
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Gated-scnn: Gated shape cnns for semantic segmentation, 2019
Towaki Takikawa, David Acuna, Varun Jampani, and Sanja Fidler · 2019
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