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Heart chambers and whole heart segmentation techniques: review
2012
Journal of Electronic Imaging (JEI)
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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]
2019
arXiv
pre-print
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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
1992
IMIA Yearbook of Medical Informatics
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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
2018
Journal of the Royal Society Interface
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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
2017
Medical Image Analysis
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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/j.media.2016.07.009
pmid:27475911
pmcid:PMC5099118
fatcat:mmmolbl4dzbbzibtjh7nmot6hm
Opportunities And Obstacles For Deep Learning In Biology And Medicine
[article]
2017
bioRxiv
pre-print
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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
2014
Progress in retinal and eye research
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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
2016
Advances in Data Analysis and Classification
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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]
2016
arXiv
pre-print
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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
2014
Medical Imaging 2014: Physics of Medical Imaging
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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]
2016
arXiv
pre-print
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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
2012
Medical Image Analysis
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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/j.media.2012.02.002
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
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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
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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
2017
Programme Grants for Applied Research
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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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