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An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net)

Khawla Brahim, Tewodros Weldebirhan Arega, Arnaud Boucher, Stephanie Bricq, Anis Sakly, Fabrice Meriaudeau
2022 Sensors  
Importantly, compared to various deep learning-based methods participating in the EMIDEC challenge, the results of our approach have a more significant agreement with manual contouring in segmenting myocardial  ...  and microvascular-obstructed (MVO) tissues from late gadolinium enhancement magnetic resonance (LGE-MR) images.  ...  Despite the potential of deep learning for several fields, few deep learning-based methodologies have been proposed in the literature for infarct segmentation from LGE-MRI. Fahmy et al.  ... 
doi:10.3390/s22062084 pmid:35336258 pmcid:PMC8954140 fatcat:46rpykoa75awppe5zvmvd335na

Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge [article]

Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa (+21 others)
2021 arXiv   pre-print
First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction.  ...  Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of  ...  of the infarction evaluated from delayed-enhancement MRI (DE-MRI) ( [37, 25] ).  ... 
arXiv:2108.04016v2 fatcat:2kxgjecvrrc3norhcq26t722we

Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information [article]

Ana Lourenço, Eric Kerfoot, Irina Grigorescu, Cian M Scannell, Marta Varela, Teresa M Correia
2020 arXiv   pre-print
In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR.  ...  Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability.  ...  Introduction Delayed-enhancement cardiac magnetic resonance (DE-CMR) is considered the non-invasive gold standard for assessing myocardial infarction and viability in coronary artery disease [3, 5, 19  ... 
arXiv:2010.08469v1 fatcat:rewnd3n5xnhtneqgu2mdmzohd4

Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle, Thibault Leclercq, Alexandre Cochet, Alain Lalande
2021 Algorithms  
In this work, a new automatic method for MI quantification from LGE-MRI is proposed.  ...  Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment.  ...  Acknowledgments: EDLR received an Erasmus+ scholarship from the Erasmus Mundus Joint Master Degree in Medical Imaging and Applications (MAIA), a program funded by the European Union.  ... 
doi:10.3390/a14080249 fatcat:ejqop3fnczbqjkpgccezuog4zi

Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation [article]

Didier R.P.R.M. Lustermans, Sina Amirrajab, Mitko Veta, Marcel Breeuwer, Cian M. Scannell
2021 arXiv   pre-print
Conclusion: A cascaded deep learning-based pipeline trained with augmentation by synthetically generated data leads to myocardium and scar segmentations that are similar to the manual operator, and outperforms  ...  The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing.  ...  The pipeline was trained based on manual segmentations of publicly available LGE cardiac MR images from the automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI (EMIDEC) challenge  ... 
arXiv:2109.12940v1 fatcat:ybqacf2xxjfgxaprbahz3o7aqy

3.0T Contrast-enhanced whole-heart coronary magnetic resonance angiography for simultaneous coronary artery angiography and myocardial viability in chronic myocardial infarction: A single-center preliminary study

Zhiyong Chen, Bin Sun, Qing Duan, Yunjing Xue, Lianglong Chen
2018 Medicine  
with standard delayed-enhancement coronary magnetic resonance (DE-CMR) for the determination of infarct size.We studied 42 consecutive patients (37 men, 5 women, mean age 58.5 ± 10.7 years) with MI scheduled  ...  reliable detection of significant obstructive coronary artery disease in patients with myocardial infarction, but also could identify and quantify the volume of myocardial infarction.  ...  Moreover, delayed-enhancement magnetic resonance imaging (DE-MRI) is a well established noninvasive imaging modality that allows assessment of myocardial infarct size.  ... 
doi:10.1097/md.0000000000013138 pmid:30407340 pmcid:PMC6250500 fatcat:2mgeycobmjdqzcvr7widinbvhu

Multi-center, multi-vendor automated segmentation of left ventricular anatomy in contrast-enhanced MRI [article]

Carla Sendra-Balcells, Víctor M. Campello, Carlos Martín-Isla, David Vilades Medel, Martín Luís Descalzo, Andrea Guala, José F. Rodríguez Palomares, Karim Lekadir
2021 arXiv   pre-print
of myocardial infarction.  ...  Many deep-learning techniques have been proposed to perform automatic segmentations of the left ventricle (LV) in LGE-MRI showing segmentations as accurate as those obtained by expert cardiologists.  ...  EMIDEC dataset: University Hospital Dijon, France This dataset was compiled as part of the automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI challenge (EMIDEC) [14] .  ... 
arXiv:2110.07360v2 fatcat:6udrjjyurra6va6lnywtlrtpay

Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease

Haoxuan Lu, Yudong Yao, Li Wang, Jianing Yan, Shuangshuang Tu, Yanqing Xie, Wenming He, Luminita Moraru
2022 Computational and Mathematical Methods in Medicine  
The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues.  ...  years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based  ...  The automatic segmentation and rearrangement of myocardial tissue by CNN algorithm for the automatic identification of infarcted myocardial tissue by features such as image texture is of great significance  ... 
doi:10.1155/2022/3016532 pmid:35516452 pmcid:PMC9064517 fatcat:nuutmmt76ndl7kcgyi6lm7ljne

A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification

Markus Huellebrand, Matthias Ivantsits, Lennart Tautz, Sebastian Kelle, Anja Hennemuth
2022 Frontiers in Cardiovascular Medicine  
The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data.  ...  We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges.  ...  MI and MH: machine learning implementation and validation of machine learning methods. MH, MI, and AH: writing-original draft. All authors helped in conceptualization and writing-review and editing.  ... 
doi:10.3389/fcvm.2022.829512 pmid:35360025 pmcid:PMC8960112 fatcat:i5724wrqz5gxzmjv67vivp2via

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

Zakarya Farea Shaaf, Muhammad Mahadi Abdul Jamil, Radzi Ambar, Ahmed Abdu Alattab, Anwar Ali Yahya, Yousef Asiri
2022 Diagnostics  
The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset.  ...  Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images.  ...  Table 1 summarizes the most recent studies in LV segmentation from short-axis MRI using deep learning models.  ... 
doi:10.3390/diagnostics12020414 pmid:35204504 pmcid:PMC8871002 fatcat:vvcwdd34uzhktiq6dmybk5hzye

Front Matter: Volume 10576

Robert J. Webster, Baowei Fei
2018 Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling  
lines to delayed contrast-enhanced magnetic resonance images for assessment of myocardial lesion formation following proton beam therapy [10576-15] 10576 0H Technical note: on-the-fly augmented reality  ...  infarct tissue using a composite material model [10576-38] 10576 1C A machine learning approach for biomechanics-based tracking of lung tumor during external beam radiation therapy [10576-39] 10576  ... 
doi:10.1117/12.2323924 fatcat:hva4ny4ftbe2voqifpbt5wewky

Deep Learning in Spatiotemporal Cardiac Imaging: A Review of Methodologies and Clinical Usability

Karen Andrea Lara Hernandez, Theresa Rienmüller, Daniela Baumgartner, Christian Baumgartner
2020 Computers in Biology and Medicine  
In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis.  ...  In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts.  ...  [30] Assessment of myocardial infarction 73 subjects 73 CMR cine (12 frames) Views: short-axis Optical flow Manual annotations obtained from delayed enhancement images SDAE and SVM Train  ... 
doi:10.1016/j.compbiomed.2020.104200 pmid:33421825 fatcat:ltxjpt6yhzgvdkifo4wo3ftveq

Segmentation of liver lesions without contrast agents with Radiomics-guided Densely-UNet-Nested GAN

Xiaojiao Xiao, Yan Qiang, Juanjuan Zhao, Xingyu Yang, XiaoTang Yang
2020 IEEE Access  
Although some deep-learning based works have attempted for liver lesions segmentation, they are all limited to the use of contrast-enhanced MRI.  ...  To avoid the limitations comes from CA, we proposed a Radiomics-guided Densely-UNet-Nested Generative Adversarial Networks (Radiomics-guided DUN-GAN) for automatic segmentation of liver lesions on non-contrast  ...  [8] proposed simultaneous segmentation and quantification of myocardial infarction (MI) without contrast agents.  ... 
doi:10.1109/access.2020.3047429 fatcat:sodjmyc3xva2lmcueugp6aru2m

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach

Ahmed S Fahmy, Ethan J Rowin, Raymond H Chan, Warren J Manning, Martin S Maron, Reza Nezafat
2021 Journal of Magnetic Resonance Imaging  
To develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification. Retrospective.  ...  Manual and CNN-based quantifications of LGE scar burden and of myocardial volume were assessed using Pearson linear correlation coefficients (r) and Bland-Altman analysis.  ...  Our results highlight the need to develop new methods for improving the generalizability of deep learning based LGE image analyses.  ... 
doi:10.1002/jmri.27555 pmid:33599043 pmcid:PMC8359184 fatcat:nvbfjuaaevgxlemftaoew2vt2q

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

Ping Xiong, Simon Ming-Yuen Lee, Ging Chan
2022 Frontiers in Cardiovascular Medicine  
Recent advances in using deep learning (DL) for ECG screening might rekindle this hope.  ...  Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving.  ...  Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. Appl Intell.  ... 
doi:10.3389/fcvm.2022.860032 pmid:35402563 pmcid:PMC8990170 fatcat:whqj3dijl5d4vnkrv7vvhnozrq
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