Zobrazeno 1 - 10 of 498 pro vyhledávání: '"Fung Kenneth"'
4
Report
Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank
Autor: Ferdian, Edward, Suinesiaputra, Avan, Fung, Kenneth, Aung, Nay, Lukaschuk, Elena, Barutcu, Ahmet, Maclean, Edd, Paiva, Jose, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E, Young, Alistair A.
Publikováno v: Radiology: Cardiothoracic Imaging 2020; 2(1):e190032
Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged images. Methods and Materials: In this retrospective cross-sectional
Externí odkaz: http://arxiv.org/abs/2004.07064
Zobrazit plný text záznamu
6
Report
Improving the generalizability of convolutional neural network-based segmentation on CMR images
Autor: Chen, Chen, Bai, Wenjia, Davies, Rhodri H., Bhuva, Anish N., Manisty, Charlotte, Moon, James C., Aung, Nay, Lee, Aaron M., Sanghvi, Mihir M., Fung, Kenneth, Paiva, Jose Miguel, Petersen, Steffen E., Lukaschuk, Elena, Piechnik, Stefan K., Neubauer, Stefan, Rueckert, Daniel
Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with h
Externí odkaz: http://arxiv.org/abs/1907.01268
Zobrazit plný text záznamu
7
Report
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
Autor: Zheng, Qiao, Delingette, Hervé, Fung, Kenneth, Petersen, Steffen E., Ayache, Nicholas
We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9
Externí odkaz: http://arxiv.org/abs/1902.05811
Zobrazit plný text záznamu
8
Report
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Autor: Robinson, Robert, Valindria, Vanya V., Bai, Wenjia, Oktay, Ozan, Kainz, Bernhard, Suzuki, Hideaki, Sanghvi, Mihir M., Aung, Nay, Paiva, Jos$é$ Miguel, Zemrak, Filip, Fung, Kenneth, Lukaschuk, Elena, Lee, Aaron M., Carapella, Valentina, Kim, Young Jin, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E., Page, Chris, Matthews, Paul M., Rueckert, Daniel, Glocker, Ben
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive
Externí odkaz: http://arxiv.org/abs/1901.09351
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje