Výsledky vyhledávání - "Martin Rajchl"

Akademický článek

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Publikováno v: Journal of Cardiovascular Magnetic Resonance, Vol 20, Iss 1, Pp 1-12 (2018)

Abstract Background Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection f

Externí odkaz: https://doaj.org/article/d747f65dcc3043c6921822cc293970e3

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EPCO-15. LINKING HISTOLOGICAL GLIOBLASTOMA PHENOTYPES TO TRANSCRIPTIONAL SUBTYPES AND PROGNOSIS USING DEEP LEARNING

Autor: Thomas Roetzer-Pejrimovsky, Barbara Kiesel, Karl-Heinz Nenning, Johanna Klughammer, Martin Rajchl, Christoph Bock, Johannes Hainfellner, Bernhard Baumann, Georg Langs, Adelheid Woehrer

Publikováno v: Neuro-Oncology. 24:vii118-vii119

Deep learning is increasingly utilized in medical image analysis and shows promise to substantially inform treatment decisions in clinical practice. While it finds broad applications in radiology or genomics, there are still relatively few applicatio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6335c615205aef4db7a7bde1c435e25b
https://doi.org/10.1093/neuonc/noac209.450

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Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi‐planar U‐Net (CMPU‐Net)

Autor: Fatemeh Zabihollahy, Martin Rajchl, James A. White, Eranga Ukwatta

Publikováno v: Medical Physics. 47:1645-1655

Purpose: Three-dimensional (3D) late gadolinium enhancement magnetic resonance (LGE-MR) imaging enables the quantification of myocardial scar at high resolution with unprecedented volumetric visualization. Automated segmentation of myocardial scar is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b0024ddc03f4bfdc06add43cbca3f73
https://doi.org/10.1002/mp.14022

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Metric learning with spectral graph convolutions on brain connectivity networks

Autor: Daniel Rueckert, Enzo Ferrante, Sarah Parisot, Matthew C. H. Lee, Sofia Ira Ktena, Ben Glocker, Martin Rajchl

Publikováno v: NeuroImage. 169:431-442

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model str

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::388592b463974eaf63d496ed2a36d068
https://doi.org/10.1016/j.neuroimage.2017.12.052

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Contribution of mitral valve leaflet length and septal wall thickness to outflow tract obstruction in patients with hypertrophic cardiomyopathy

Autor: Peter Giannoccaro, Martin Rajchl, Kareem Morant, Andrew Grant, Immaculate Nevis, David McCarty, David Scholl, Andrew G Howarth, John Stirrat, Carmen P Lydell, James A. White, Yoko Mikami, Louis Kolman, B. Heydari

Publikováno v: The International Journal of Cardiovascular Imaging. 33:1201-1211

We sought to examine whether elongation of the mitral valve leaflets in patients with hypertrophic cardiomyopathy (HCM) is synergistic to septal wall thickness (SWT) in the development of left ventricular outflow tract obstruction (LVOTO). HCM is a c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74eb9e88ce7515187cefb68ca5a31640
https://doi.org/10.1007/s10554-017-1103-5

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Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Autor: Veronika A. Zimmer, Matthew Sinclair, Daniel Rueckert, Alberto Gomez, James Housden, Jo Schlemper, Ozan Oktay, Jacqueline Matthew, Bernhard Kainz, Benjamin Hou, Julia A. Schnabel, Nicolas Toussaint, Martin Rajchl, Qingjie Meng

Publikováno v: Meng, Q, Sinclair, M, Zimmer, V, Hou, B, Rajchl, M, Toussaint, N, Oktay, O, Schlemper, J, Gomez, A, Housden, J, Matthew, J, Rueckert, D, Schnabel, J A & Kainz, B 2019, ' Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging ', IEEE transactions on medical imaging, vol. 38, no. 12, 8698843, pp. 2755-2767 . https://doi.org/10.1109/TMI.2019.2913311

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provid

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::512a6b1ee72845511912a59c30300847
https://pubmed.ncbi.nlm.nih.gov/31021795

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Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling

Autor: Terry M. Peters, Wu Qiu, Martin Rajchl, A. Jonathan McLeod, John S. H. Baxter, Ali R. Khan, Jing Yuan

Publikováno v: Medical Biophysics Publications

© 2015 Elsevier B.V. The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmenta

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76d804f99a6b26817f86aa76a23779d2
https://doi.org/10.1016/j.media.2015.05.005

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Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease

Autor: Wu Qiu, Aaron Fenster, Jing Yuan, Eranga Ukwatta, Bernard Chiu, Martin Rajchl

Publikováno v: Medical Image Analysis. 26:120-132

Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3c0b821fa6fd2b15f33db3e8192b840
https://doi.org/10.1016/j.media.2015.08.004

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Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Autor: Stuart A. Cook, Antonio de Marvao, Giacomo Tarroni, Daniel Rueckert, Martin Rajchl, Ozan Oktay, Carlo Biffi, Georgia Doumou, Reem Bedair, Wenjia Bai, Declan P. O'Regan, Sanjay K Prasad

Publikováno v: International Conference On Medical Image Computing & Computer Assisted Intervention
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009335
MICCAI (2)

Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analys

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56178049ca076b222e7631513dfb013d
https://openaccess.city.ac.uk/id/eprint/23148/1/1807.06843v1.pdf

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Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI

Autor: Daniel Rueckert, Martin Rajchl, Ben Glocker, Ioannis Lavdas, Vanya V. Valindria, Nick Pawlowski, Andrea Rockall, Eric O. Aboagye

Publikováno v: IEEE Winter Conference on Applications of Computer Vision
WACV

Convolutional neural networks have been widely used in medical image segmentation. The amount of training data strongly determines the overall performance. Most approaches are applied for a single imaging modality, e.g., brain MRI. In practice, it is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bc3a7f9820296735099135c77a011d3
http://hdl.handle.net/10044/1/56452

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