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Heart chambers and whole heart segmentation techniques: review

Dongwoo Kang
2012 Journal of Electronic Imaging (JEI)  
Computer-aided segmentation of cardiac images obtained by various modalities plays an important role and is a prerequisite for a wide range of cardiac applications by facilitating the delineation of anatomical  ...  We provide an overview of cardiac segmentation techniques, with a goal of providing useful advice and references.  ...  be combined with various models such as the hidden Markov random field model in order to achieve accurate and robust segmentation results. 62 However, clustering-based methods have a few weaknesses.  ... 
doi:10.1117/1.jei.21.1.010901 fatcat:44rwjcigqbdqzdfj7hiog4mddq

Deep learning for cardiac image segmentation: A review [article]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2019 arXiv   pre-print
Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential  ...  (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels).  ...  An online resource 2 is referred here, which illustrates and visualizes the change of receptive field by varying the number of hidden layers and the size of kernels.  ... 
arXiv:1911.03723v1 fatcat:cwsq5hiaebgkza5ktmtyw553je

Synopsis of Scientific Contributions

Jan H. van Bemmel, Alexa T. McCray
1992 IMIA Yearbook of Medical Informatics  
Tiley recommend the evaluation of systems other than the one they assessed themselves-a study that could follow the same lines of assessment as re- oped a hidden Markov model and applied automated clipping  ...  Scans were made in three directions for a field of 15 em, with projections in the axial, coronal, and sagittal planes.  ... 
doi:10.1055/s-0038-1637966 fatcat:6ms2xll4p5axxghjh53lhuvub4

Opportunities and obstacles for deep learning in biology and medicine

Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen (+24 others)
2018 Journal of the Royal Society Interface  
These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood.  ...  Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features.  ...  We thank Aaron Sheldon, who contributed text but did not formally approve the manuscript; Anna Greene for a careful proofreading of the manuscript in advance of the first submission; Sebastian Raschka  ... 
doi:10.1098/rsif.2017.0387 pmid:29618526 pmcid:PMC5938574 fatcat:65o4xmp53nc6zmj37srzuht6tq

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Oskar Maier, Bjoern H. Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich, Matthias Liebrand, Stefan Winzeck, Abdul Basit, Paul Bentley, Liang Chen, Daan Christiaens, Francis Dutil (+37 others)
2017 Medical Image Analysis  
A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges.  ...  A total of 16 research groups participated with a wide range of state-ofthe-art automatic segmentation algorithms.  ...  convolutional neural network (CNN), Markov Random Field (MRF), Conditional Random Field (CRF) and expectation maximization (EM).  ... 
doi:10.1016/ pmid:27475911 pmcid:PMC5099118 fatcat:mmmolbl4dzbbzibtjh7nmot6hm

Opportunities And Obstacles For Deep Learning In Biology And Medicine [article]

Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, Wei Xie, Gail L. Rosen (+24 others)
2017 bioRxiv   pre-print
Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains.  ...  We examine applications of deep learning to a variety of biomedical problems - patient classification, fundamental biological processes, and treatment of patients - and discuss whether deep learning will  ...  We would like to thank Anna Greene for a careful proofreading of the manuscript in advance of the first submission.  ... 
doi:10.1101/142760 fatcat:l7zvbtbgxjamtd735vir2trw6q

Progress on retinal image analysis for age related macular degeneration

Yogesan Kanagasingam, Alauddin Bhuiyan, Michael D. Abràmoff, R. Theodore Smith, Leonard Goldschmidt, Tien Y. Wong
2014 Progress in retinal and eye research  
detection and/or quantification for measurement of AMD severity using these imaging modalities.  ...  Each of these imaging modalities has strengths and weaknesses for extracting individual AMD pathology and different imaging techniques are used in combination for capturing and/or quantification of different  ...  ., 2001) proposed an automated algorithm for retinal thickness measurement from OCT images using the Markov boundary model.  ... 
doi:10.1016/j.preteyeres.2013.10.002 pmid:24211245 fatcat:32kzwud635aexpvelxusqkqyzm

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
Both methods are applied to parameter estimation of a hidden Markov random field, and are compared to the standard data augmentation approach.  ...  prognosis and clinical management of acute myocardial infarction patients, an accurate assessment of the different prognostic factors is required.  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4

Extraction of clinical information from the non-invasive fetal electrocardiogram [article]

Joachim Behar
2016 arXiv   pre-print
In order to advance the field of NI-FECG signal processing, the development of standardised public databases and benchmarking of a number of published and novel algorithms was necessary.  ...  The main challenge with NI-FECG extraction techniques is the low signal-to-noise ratio of the FECG signal on the abdominal mixture signal which consists of a dominant maternal ECG component, FECG and noise  ...  [93] and subsequently used in Oster et al. [94] . It makes use of a Hidden Markov Model to switch between normal and ectopic beats.  ... 
arXiv:1606.01093v1 fatcat:y7lbzkxxifdibctf2bljfgxfoe

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
We aimed to develop and validate a fully-automated, accurate algorithm for EVF quantification from non-contrast CT using active atlas.  ...  Many penalty terms have been proposed like q-generalized Gaussian Markov random fields,total variation (TV) and Huber penalty or other anisotropic filters.  ...  This is a manual process and can be time consuming in cases where several sections using different stains are required.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

Segmentation of Soft atherosclerotic plaques using active contour models [article]

Muhammad Moazzam Jawaid
2016 arXiv   pre-print
Detection of non-calcified plaques in the coronary tree is a challenging problem due to the nature of comprising substances.  ...  In the following step the behaviour of contrast agent has been modelled mathematically to reflect the dye diffusion in respective CTA volume.  ...  Individual or combined application of clustering, Markov random field, artificial neural networks, PDE based deformable models, region growing and the simplest thresholding have been reported in literature  ... 
arXiv:1608.00116v1 fatcat:5h6n3ervbze47b6oiit66ro4qq

Image-based characterization of thrombus formation in time-lapse DIC microscopy

Nicolas Brieu, Nassir Navab, Jovana Serbanovic-Canic, Willem H. Ouwehand, Derek L. Stemple, Ana Cvejic, Martin Groher
2012 Medical Image Analysis  
These two features are derived from the modeling of motion patterns using dense motion fields (Optic-flow) and Linear Dynamic Systems (LDS).  ...  From a mathematical point of view, the above developments of two DT-based likelihoods lead us to propose a novel approximation of the normal distribution on the manifold of DT models.  ...  The authors further extend the MDT model to the Layered Dynamic Texture (LDT) model [8] in which the spatial coherence of the DT models is ensured using a Markov random field for u.  ... 
doi:10.1016/ pmid:22482997 pmcid:PMC3740235 fatcat:dvg3ync4ejg5deeofot6mhs5hy

CARS 2016—Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition Heidelberg, Germany, June 21–25, 2016

2016 International Journal of Computer Assisted Radiology and Surgery  
We would like to thank the Spanish company BQ for the donation of the 3D printing hardware for clinical use.  ...  Acknowledgments The authors wish to thank Fundación CEIBA and Alcaldía Mayor de Bogotá, for the financial support of Ricardo Mendoza's PhD studies through the scholarship program ''Becas Rodolfo Llinás  ...  Model (GMM) and Hidden Markov Random Field (HMRF).  ... 
doi:10.1007/s11548-016-1412-5 pmid:27206418 fatcat:uk5r46n2xvhedkfjzmeiweyneq

Video Imaging of Fibrillation and Defibrillation [chapter]

2002 Quantitative Cardiac Electrophysiology  
The information or guidance contained in this book is intended for use by medical, scientific or health-care professionals and is provided strictly as a supplement to the medical or other professional's  ...  of the publishers.  ...  Thus, in the literature, we find several non-HH models of activation that use the Markov process formalism [81] [82] [83] [84] [85] [86] .  ... 
doi:10.1201/b14064-20 fatcat:e45bxiosojbmbhotaroffyf7ri

Diagnostic and therapeutic medical devices for safer blood management in cardiac surgery: systematic reviews, observational studies and randomised controlled trials

Gavin J Murphy, Andrew D Mumford, Chris A Rogers, Sarah Wordsworth, Elizabeth A Stokes, Veerle Verheyden, Tracy Kumar, Jessica Harris, Gemma Clayton, Lucy Ellis, Zoe Plummer, William Dott (+7 others)
2017 Programme Grants for Applied Research  
The routine use of POC tests was not cost-effective. A systematic review concluded that POC-based algorithms are not clinically effective.  ...  Objective To evaluate the clinical effectiveness and cost-effectiveness of medical devices used as diagnostic and therapeutic tools for the management of anaemia and bleeding in cardiac surgery.  ...  of the evidence used to support the current NICE guidance, and to place the results of the COPTIC study in context, we undertook a systematic review of RCTs that have assessed the clinical efficacy of  ... 
doi:10.3310/pgfar05170 fatcat:vufsh4dwb5dy7ehfokwttva5yq
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