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End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation [article]

Nhat M. Nguyen, Nilanjan Ray
2019 arXiv   pre-print
We apply SG to combine convolutional neural network (CNN) with dynamic programming (DP) in end-to-end learning for segmenting left ventricle from short axis view of heart MRI.  ...  Differentiable programming is able to combine different functions or programs in a processing pipeline with the goal of applying end-to-end learning or optimization.  ...  Acknowledgements Authors acknowledge funding support from NSERC and Computing Science, University of Alberta.  ... 
arXiv:1812.00328v2 fatcat:u7xoiv75o5bjnnxje34pnoyqqu

Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

Meijun Yang, Xiaoyan Xiao, Zhi Liu, Longkun Sun, Wei Guo, Lizhen Cui, Dianmin Sun, Pengfei Zhang, Guang Yang
2020 Scientific Programming  
of clinicians and improve the reproducibility of left ventricle segmentation.  ...  We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three  ...  More importantly, this work is of great significance for the three-dimensional reconstruction of the heart chamber and left ventricular segmentation. 2 Scientific Programming Materials and Methods  ... 
doi:10.1155/2020/7025403 fatcat:cq2pdns2wrab7niuzqjnaxwfxq

Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference [article]

Alireza Norouzi, Ali Emami, S.M.Reza Soroushmehr, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
2018 arXiv   pre-print
For a real use-case, we apply this method to left ventricle segmentation in MRI cardiac images. We also propose an adaptive thresholding method to consider the deep neural network uncertainty.  ...  We calculate simple random transformations to estimate the prediction uncertainty of deep convolutional neural networks.  ...  More recently, a method based on dynamic programming has been proposed to segment the left ventricle in cardiac MRI [9] .  ... 
arXiv:1803.00406v1 fatcat:52gnxvkzmfeaxjgpmksbghh4wa

Automatic segmentation and cardiac mechanics analysis of evolving zebrafish using deep-learning [article]

Bohan Zhang, Kristofor Pas, Toluwani Ijaseun, Hung Cao, Peng Fei, Juhyun Lee
2021 bioRxiv   pre-print
In addition to understanding the cardiac function for each of the two chambers, the ventricle and atrium were separated by 3D erode morphology methods.  ...  Interestingly, stroke volume (SV) remains similar in the atrium while that of the ventricle increases SV gradually.ConclusionOur U-net based segmentation provides a delicate method to segment the intricate  ...  To begin, the parameters of the U-net could be tweaked to increase the accuracy and precision of the program for the zebrafish hearts, this would lead to a better quality auto-segmentation, which would  ... 
doi:10.1101/2021.02.21.432186 fatcat:jtuuxniah5gqhnshs73z6zzsky

Automatic Segmentation and Cardiac Mechanics Analysis of Evolving Zebrafish Using Deep Learning

Bohan Zhang, Kristofor E. Pas, Toluwani Ijaseun, Hung Cao, Peng Fei, Juhyun Lee
2021 Frontiers in Cardiovascular Medicine  
Interestingly, stroke volume (SV) remains similar in the atrium while that of the ventricle increases SV gradually.Conclusion: Our U-net-based segmentation provides a delicate method to segment the intricate  ...  In addition to understanding each of the two chambers' cardiac function, the ventricle and atrium were separated by 3D erode morphology methods.  ...  FIGURE 1 | 1 U-net convolution neural network (CNN) architecture utilized to generate the binary mask of the intracardiac domain of zebrafish.  ... 
doi:10.3389/fcvm.2021.675291 pmid:34179138 pmcid:PMC8221393 fatcat:u3hjrbmdfze2lcv7znb42lsrju

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  attention in acute ischemic stroke [11313-77] 11313 27 A grid-line suppression technique based on deep convolutional neural networks [11313-78] 11313 28 An end-to-end deep learning approach for  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Cardiac MR segmentation based on sequence propagation by deep learning

Chao Luo, Canghong Shi, Xiaoji Li, Dongrui Gao, Dzung Pham
2020 PLoS ONE  
In this paper, we propose a method for CMR segmentation based on U-Net and combined with image sequence information.  ...  Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis.  ...  Kong et al. proposed a method of combining 2D CNN with recurrent neural network (RNN) to identify end-diastolic and end-systolic phases [17] .  ... 
doi:10.1371/journal.pone.0230415 pmid:32271777 fatcat:sjufdtxmn5bpji3trjt5iio4y4

Seg-CapNet: A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

Yang-Jie Cao, Shuang Wu, Chang Liu, Nan Lin, Yuan Wang, Cong Yang, Jie Li
2021 Journal of Computer Science and Technology  
For example, the predicted endocardium of the cardiac left ventricle may intersect with the predicted epicardium of the left ventricle.  ...  Finally, we calculate end-systolic volume (ESV) which is a clinical index of the left ventricle to further illustrate the superiority of Seg-CapNet.  ... 
doi:10.1007/s11390-021-0782-5 pmid:33867774 pmcid:PMC8044657 fatcat:5wrgyohn5rcnlawhiszqz4tce4

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

Zisang Zhang, Ye Zhu, Manwei Liu, Ziming Zhang, Yang Zhao, Xin Yang, Mingxing Xie, Li Zhang
2022 Journal of Clinical Medicine  
The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial  ...  Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing.  ...  The results showed that the EchoNet-Dynamic segmented the left ventricle accurately with a Dice similarity coefficient greater than 0.9, both at the end-systole and end-diastole levels, as well as across  ... 
doi:10.3390/jcm11102893 fatcat:xh3lhwywdneojmtfmppvzwfa7y

Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning [article]

Bosung Seo, Daniel Mariano, John Beckfield, Vinay Madenur, Yuming Hu, Tony Reina, Marcus Bobar, Mai H. Nguyen, Ilkay Altintas
2019 arXiv   pre-print
The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation.  ...  We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose.  ...  Findings The first finding we had was that it is possible to get good results for segmenting the left and right ventricle using any of 3D U-Net, 2D U-Net or DenseNet.  ... 
arXiv:1909.08028v1 fatcat:wlk7tvn72vhkrd22ipdp6ydezq

Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images [article]

Garvit Chhabra, J. H. Gagan, J. R. Harish Kumar
2022 arXiv   pre-print
We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images.  ...  Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction.  ...  Chandra Sekhar Seelamantula, Department of Electrical Engineering, Indian Institute of Science, Bangalore for his guidance and support during the research.  ... 
arXiv:2201.12805v1 fatcat:7qxylovxojaa5agajzrvonu3ay

Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks

Taeouk Kim, Mohammadali Hedayat, Veronica V. Vaitkus, Marek Belohlavek, Vinayak Krishnamurthy, Iman Borazjani
2021 Quantitative Imaging in Medicine and Surgery  
Boundary identification of left ventricle (LV) in 2D echo, i.e., image segmentation, is the first step to calculate relevant clinical parameters.  ...  We evaluated the performance of the state-of-the-art convolutional neural networks (CNNs) for the segmentation of 2D echo images from 6 standard projections of the LV.  ...  This revolution has primarily been powered by supervised machine learning with convolutional neural networks (CNNs).  ... 
doi:10.21037/qims-20-745 pmid:33936963 pmcid:PMC8047352 fatcat:tejgnfuhkzdz7bgrcgofxsovbu

Automated segmentation on the entire cardiac cycle using a deep learning work-flow [article]

Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana
2018 arXiv   pre-print
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters.  ...  Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole.  ...  Segmentation of the Left Ventricle (LV) from CMR images provides a standard procedure for the determination of cardiac parameters which is time-consuming and requires relevant experience.  ... 
arXiv:1809.01015v1 fatcat:jwv3zu34u5brdow2wosp7za2qa

Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation

Kyeorye Lee, Leonard Sunwoo, Tackeun Kim, Kyong Joon Lee
2021 Applied Sciences  
Automation of 3D BVS using deep supervised learning is being researched, and U-Net-based approaches, which are considered as standard for medical image segmentation, are proposed a lot.  ...  Spider U-Net outperformed 2D U-Net, 3D U-Net, and the fully convolutional network-recurrent neural network (FCN-RNN) in dice coefficient score (DSC) by 0.048, 0.077, and 0.041, respectively, for our in-house  ...  The last dataset was the public cardiac MRI dataset for left ventricle (LV) segmentation provided by York university [25] .  ... 
doi:10.3390/app11052014 fatcat:hjfnwrx5jzeexjmqj3kj5ucnj4

Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning [article]

Ehab Abdelmaguid, Jolene Huang, Sanjay Kenchareddy, Disha Singla, Laura Wilke, Mai H. Nguyen, Ilkay Altintas
2018 arXiv   pre-print
The end-systolic volume (ESV) and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection fraction (EF) are indicators of heart disease.  ...  An end-to-end analytics pipeline with multiple stages is provided for automated LV segmentation and volume estimation.  ...  Acknowledgements The authors would like to thank the following individuals for fruitful discussions related to this project: Marcus Bobar, Eric Carruth, Dmitry Mishin, Evan Muse, and Gary Cottrell.  ... 
arXiv:1809.06247v2 fatcat:ovqgoajju5g2lf4taajn6sytqe
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