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49 results on '"Phellan, Renzo"'

1. The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study

Author

Phellan, Renzo, Lindner, Thomas, Helle, Michael, Falcão, Alexandre X., Forkert, Nils D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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2. Four-Dimensional ASL MR Angiography Phantoms with Noise Learned by Neural Styling

Author

Phellan, Renzo, Linder, Thomas, Helle, Michael, Spina, Thiago V., Falcão, Alexandre, Forkert, Nils D., Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Stoyanov, Danail, editor, Taylor, Zeike, editor, Balocco, Simone, editor, Sznitman, Raphael, editor, Martel, Anne, editor, Maier-Hein, Lena, editor, Duong, Luc, editor, Zahnd, Guillaume, editor, Demirci, Stefanie, editor, Albarqouni, Shadi, editor, Lee, Su-Lin, editor, Moriconi, Stefano, editor, Cheplygina, Veronika, editor, Mateus, Diana, editor, Trucco, Emanuele, editor, Granger, Eric, editor, and Jannin, Pierre, editor

Published
2018
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4. Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network

Author

Phellan, Renzo, Peixinho, Alan, Falcão, Alexandre, Forkert, Nils D., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Lee, Su-Lin, editor, Cheplygina, Veronika, editor, Balocco, Simone, editor, Mateus, Diana, editor, Zahnd, Guillaume, editor, Maier-Hein, Lena, editor, Demirci, Stefanie, editor, Granger, Eric, editor, Duong, Luc, editor, Carbonneau, Marc-André, editor, Albarqouni, Shadi, editor, and Carneiro, Gustavo, editor

Published
2017
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6. Improving Atlas-Based Medical Image Segmentation with a Relaxed Object Search

Author

Phellan, Renzo, Falcão, Alexandre X., Udupa, Jayaram, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Zhang, Yongjie Jessica, editor, and Tavares, João Manuel R. S., editor

Published
2014
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12. Real-time biomechanics using the finite element method and machine learning: Review and perspective

Author

Phellan, Renzo, Hachem, Bahe, Clin, Julien, Mac-Thiong, Jean-Marc, Duong, Luc, Phellan, Renzo, Hachem, Bahe, Clin, Julien, Mac-Thiong, Jean-Marc, and Duong, Luc

Abstract

Purpose -- The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results. Methods -- This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains. Results -- A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML. Conclusions -- ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice.

Published
2021

15. Association of cardiovascular risk factors with MRI indices of cerebrovascular structure and function and white matter hyperintensities in young adults

Author

Williamson, Wilby, Lewandowski, Adam J., Forkert, Nils D., Griffanti, Ludo, Okell, Thomas W., Betts, Jill, Boardman, Henry, Siepmann, Timo, McKean, David, Huckstep, Odaro, Francis, Jane, Neubauer, Stefan, Phellan, Renzo, Jenkinson, Mark, Doherty, Aiden, Dawes, Helen, Frangou, Eleni, Malamateniou, Christina, Foster, Charlie, Leeson, Paul, Williamson, Wilby, Lewandowski, Adam J., Forkert, Nils D., Griffanti, Ludo, Okell, Thomas W., Betts, Jill, Boardman, Henry, Siepmann, Timo, McKean, David, Huckstep, Odaro, Francis, Jane, Neubauer, Stefan, Phellan, Renzo, Jenkinson, Mark, Doherty, Aiden, Dawes, Helen, Frangou, Eleni, Malamateniou, Christina, Foster, Charlie, and Leeson, Paul

Abstract

Importance: Risk of stroke and brain atrophy in later life relate to levels of cardiovascular risk in early adulthood. However, it is unknown whether cerebrovascular changes are present in young adults. Objective: To examine relationships between modifiable cardiovascular risk factors and cerebrovascular structure, function, and white matter integrity in young adults. Design, Setting, and Participants: A cross-sectional observational study of 125 young adults (aged 18-40 years) without clinical evidence of cerebrovascular disease. Data collection was completed between August 2014 and May 2016 at the University of Oxford, United Kingdom. Final data collection was completed on May 31, 2016. Exposures The number of modifiable cardiovascular risk factors at recommended levels, based on the following criteria: body mass index (BMI) <25; highest tertile of cardiovascular fitness and/or physical activity; alcohol consumption <8 drinks/week; nonsmoker for >6 months; blood pressure on awake ambulatory monitoring <130/80 mm Hg; a nonhypertensive diastolic response to exercise (peak diastolic blood pressure <90 mm Hg); total cholesterol <200 mg/dL; and fasting glucose <100mg/dL. Each risk factor at the recommended level was assigned a value of 1, and participants were categorized from 0-8, according to the number of risk factors at recommended levels, with higher numbers indicating healthier risk categories. Main Outcomes and Measures: Cerebral vessel density, caliber and tortuosity, brain white matter hyperintensity lesion count. In a subgroup (n = 52), brain blood arrival time and cerebral blood flow assessed by brain magnetic resonance imaging (MRI). Results: A total of 125 participants, mean (SD) age 25 (5) years, 49% women, with a mean (SD) score of 6.0 (1.4) modifiable cardiovascular risk factors at recommended levels, completed the cardiovascular risk assessment and brain MRI protocol. Cardiovascular risk factors were correlated with cerebrovascular morphology and white

Published
2018

16. Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA.

Author

Phellan, Renzo, Lindner, Thomas, Helle, Michael, Falcao, Alexandre X., Yasuda, Clarissa L., Sokolska, Magdalena, Jager, Rolf H., and Forkert, Nils Daniel

Subjects
*BLOOD flow, *SPIN labels, *MAGNETIC resonance angiography, *DIGITAL subtraction angiography, *CEREBRAL circulation, *PHANTOM limbs, *IMAGE processing, *DRUG labeling
Abstract

Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively. [ABSTRACT FROM AUTHOR]

Published
2020
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17. Association of Cardiovascular Risk Factors With MRI Indices of Cerebrovascular Structure and Function and White Matter Hyperintensities in Young Adults

Author

Williamson, Wilby, primary, Lewandowski, Adam J., additional, Forkert, Nils D., additional, Griffanti, Ludovica, additional, Okell, Thomas W., additional, Betts, Jill, additional, Boardman, Henry, additional, Siepmann, Timo, additional, McKean, David, additional, Huckstep, Odaro, additional, Francis, Jane M., additional, Neubauer, Stefan, additional, Phellan, Renzo, additional, Jenkinson, Mark, additional, Doherty, Aiden, additional, Dawes, Helen, additional, Frangou, Eleni, additional, Malamateniou, Christina, additional, Foster, Charlie, additional, and Leeson, Paul, additional

Published
2018
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23. Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models.

Author

Phellan, Renzo, Falcão, Alexandre X., and Udupa, Jayaram K.

Subjects
*IMAGE segmentation, *DIAGNOSTIC imaging, *BOUNDARY value problems, *IMAGE registration, *FUZZY logic, *MATHEMATICAL models
Abstract

Statistical object shape models (SOSMs), known as probabilistic atlases, are popular in medical image segmentation. They register an image into the atlas coordinate system, such that a desired object can be delineated from the constraints of its shape model. While this strategy facilitates segmenting objects with even weak-boundary contrast, it tends to require more models per object to cope with possible registration errors. Fuzzy object shape models (FOSMs) gain substantial speed by avoiding image registration and placing more relaxed model constraints with optimum object search. However, they tend to require stronger object boundary contrast for effective delineation. In this work, the authors show that optimum object search, the essential underpinning of FOSMs, can improve segmentation efficacy of SOSMs with fewer models per object. Methods: For the sake of efficiency, the authors use three atlases per object (SOSM-3) as baseline for segmentation based on the best match with posterior probability maps. A novel strategy for SOSM with a single atlas and optimum object search (SOSM-S) is presented. When registering an image to the atlas system, one should expect that the object's boundary falls within the uncertainty region of the model--region wherein voxels show probabilities greater than 0 and less than 1 to be in the object. Since registration may fail, SOSM-S translates the atlas locally and, at each location, delineates and scores a candidate object in the uncertainty region. Segmentation is defined by the candidate with the highest score. The presented FOSM also uses a single model per object, but model construction uses only shape translations, building a fuzzy object model with larger uncertainty region. Optimum object search requires estimation of the object's location and/or optimization algorithms to speed-up segmentation. Results: The authors evaluate SOSM-3, SOSM-S, and FOSM on 75 CT-images of the thorax and 35 MR T1-weighted images of the brain, with nine objects of interest. The results show that SOSM-S and FOSM can segment seven out of the nine objects with higher accuracy than SOSM-3, according to the average symmetric surface distance and statistical test. SOSM-S was consistently more accurate than FOSM, FOSM being 2-3 orders of magnitude faster than SOSM-S and SOSM-3 for model construction and hundreds of times faster than them for segmentation. Conclusions: Although multiple models per object can usually improve segmentation efficacy, the optimum object search has shown to reduce the number of required models. The efficiency gain of FOSM over SOSM-S motivates its use for interactive applications and studies with large image data sets. FOSM and SOSM impose different degrees of shape constraints from the model, making one approach more suitable than the other, depending on contrast. This suggests the use of hybrid models that can take advantage from the strengths of fuzzy and statistical models. [ABSTRACT FROM AUTHOR]

Published
2016
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26. Advanced Multi-objective Design Analysis to Identify Ideal Stent Design

Author

Gharleghi, Ramtin, Wright, Heidi, Khullar, Somesh, Liu, Jinbo, Ray, Tapabrata, Beier, Susann, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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27. Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation

Author

Chen, Li, Wang, Gaoang, Balu, Niranjan, Mossa-Basha, Mahmud, Zhao, Xihai, Li, Rui, He, Le, Hatsukami, Thomas S., Hwang, Jenq-Neng, Yuan, Chun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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28. Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

Author

López-Linares, Karen, Stephens, Maialen, García, Inmaculada, Macía, Iván, González Ballester, Miguel Ángel, José Estepar, Raúl San, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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29. Towards Quantifying Neurovascular Resilience

Author

Moriconi, Stefano, Rehwald, Rafael, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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30. Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography

Author

Liu, Shengnan, Shamonin, Denis P., Zahnd, Guillaume, van der Steen, A. F. W., van Walsum, Theo, van Soest, Gijs, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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31. Tracking of Intracavitary Instrument Markers in Coronary Angiography Images

Author

Zhang, Yihe, Bai, Xiuxiu, Jin, Qinhua, Jing, Jing, Chen, Yundai, Liu, Qiang, Tang, Wenhui, Lu, Quanmao, Mi, Yanan, Zhu, Rui, Cao, Yihui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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32. Random 2.5D U-net for Fully 3D Segmentation

Author

Angermann, Christoph, Haltmeier, Markus, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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33. A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN

Author

Wang, Chunli, Yang, Shan, Tang, Xun, Li, Bin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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34. Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTM

Author

Luo, Chengsi, Jiang, Hongxiu, Li, Quanchi, Rao, Nini, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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35. Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks

Author

Yuan, Binhang, Xing, Wenhui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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36. Automatic Diagnosis with 12-Lead ECG Signals

Author

Wang, Ke, Zhang, Xuan, Zhong, Haoxi, Chen, Ting, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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37. An Ensemble Neural Network for Multi-label Classification of Electrocardiogram

Author

Jia, Dongya, Zhao, Wei, Li, Zhenqi, Yan, Cong, Wang, Hongmei, Hu, Jing, Fang, Jiansheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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38. An Approach to Predict Multiple Cardiac Diseases

Author

Bin, Guanghong, Sun, Yongyue, Huang, Jiao, Bin, Guangyu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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39. Transfer Learning for Electrocardiogram Classification Under Small Dataset

Author

Chen, Longting, Xu, Guanghua, Zhang, Sicong, Kuang, Jiachen, Hao, Long, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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40. A Multi-label Learning Method to Detect Arrhythmia Based on 12-Lead ECGs

Author

Zhu, Jinjing, Xin, Kaifa, Zhao, Qingqing, Zhang, Yue, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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41. Arrhythmia Classification with Attention-Based Res-BiLSTM-Net

Author

Huang, Chengbin, Zhao, Renjie, Chen, Weiting, Li, Huazheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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42. Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans

Author

Fan, Bowen, Tomii, Naoki, Tsukihara, Hiroyuki, Maeda, Eriko, Yamauchi, Haruo, Nawata, Kan, Hatano, Asuka, Takagi, Shu, Sakuma, Ichiro, Ono, Minoru, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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43. Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

Author

Huo, Qiang, Tang, Geyu, Zhang, Feng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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44. ARVBNet: Real-Time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-Chamber Planes

Author

Dong, Jinbao, Liu, Shengfeng, Wang, Tianfu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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45. Deep Learning to Improve Heart Disease Risk Prediction

Author

Sajeev, Shelda, Maeder, Anthony, Champion, Stephanie, Beleigoli, Alline, Ton, Cheng, Kong, Xianglong, Shu, Minglei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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46. LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation

Author

Ding, Zijian, Qiu, Shan, Guo, Yutong, Lin, Jianping, Sun, Li, Fu, Dapeng, Yang, Zhen, Li, Chengquan, Yu, Yang, Meng, Long, Lv, Tingting, Li, Dan, Zhang, Ping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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47. Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention

Author

Liu, Yang, He, Runnan, Wang, Kuanquan, Li, Qince, Sun, Qiang, Zhao, Na, Zhang, Henggui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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48. Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points

Author

Xia, Zhourui, Sang, Zhenhua, Guo, Yutong, Ji, Weijie, Han, Chenguang, Chen, Yanlin, Yang, Sifan, Meng, Long, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liao, Hongen, editor, Balocco, Simone, editor, Wang, Guijin, editor, Zhang, Feng, editor, Liu, Yongpan, editor, Ding, Zijian, editor, Duong, Luc, editor, Phellan, Renzo, editor, Zahnd, Guillaume, editor, Breininger, Katharina, editor, Albarqouni, Shadi, editor, Moriconi, Stefano, editor, Lee, Su-Lin, editor, and Demirci, Stefanie, editor

Published
2019
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49. Real-time biomechanics using the finite element method and machine learning: Review and perspective.

Author

Phellan R, Hachem B, Clin J, Mac-Thiong JM, and Duong L

Subjects
Computer Simulation, Finite Element Analysis, Algorithms, Biomechanical Phenomena, Machine Learning
Abstract

Purpose: The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results., Methods: This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains., Results: A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML., Conclusions: ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice., (© 2020 American Association of Physicists in Medicine.)

Published
2021
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