Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

10.1109/TMI.2017.2743464

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Bibliographic Details
Main Authors:	Oktay O., Ferrante E., Kamnitsas K., Heinrich M., Bai W., Caballero J., Cook S.A., De Marvao A., Dawes T., O'Regan D.P., Kainz B., Glocker B., Rueckert D.
Other Authors:	DUKE-NUS MEDICAL SCHOOL
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2019
Subjects:	convolutional neural network image super-resolution medical image segmentation Shape prior
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/150626
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-150626
record_format	dspace
spelling	sg-nus-scholar.10635-1506262023-10-31T22:03:46Z Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation Oktay O. Ferrante E. Kamnitsas K. Heinrich M. Bai W. Caballero J. Cook S.A. De Marvao A. Dawes T. O'Regan D.P. Kainz B. Glocker B. Rueckert D. DUKE-NUS MEDICAL SCHOOL convolutional neural network image super-resolution medical image segmentation Shape prior 10.1109/TMI.2017.2743464 IEEE Transactions on Medical Imaging 37 2 384-395 ITMID 2019-01-08T09:00:31Z 2019-01-08T09:00:31Z 2018 Article Oktay O., Ferrante E., Kamnitsas K., Heinrich M., Bai W., Caballero J., Cook S.A., De Marvao A., Dawes T., O'Regan D.P., Kainz B., Glocker B., Rueckert D. (2018). Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Transactions on Medical Imaging 37 (2) : 384-395. ScholarBank@NUS Repository. https://doi.org/10.1109/TMI.2017.2743464 02780062 http://scholarbank.nus.edu.sg/handle/10635/150626 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	convolutional neural network image super-resolution medical image segmentation Shape prior
spellingShingle	convolutional neural network image super-resolution medical image segmentation Shape prior Oktay O. Ferrante E. Kamnitsas K. Heinrich M. Bai W. Caballero J. Cook S.A. De Marvao A. Dawes T. O'Regan D.P. Kainz B. Glocker B. Rueckert D. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
description	10.1109/TMI.2017.2743464
author2	DUKE-NUS MEDICAL SCHOOL
author_facet	DUKE-NUS MEDICAL SCHOOL Oktay O. Ferrante E. Kamnitsas K. Heinrich M. Bai W. Caballero J. Cook S.A. De Marvao A. Dawes T. O'Regan D.P. Kainz B. Glocker B. Rueckert D.
format	Article
author	Oktay O. Ferrante E. Kamnitsas K. Heinrich M. Bai W. Caballero J. Cook S.A. De Marvao A. Dawes T. O'Regan D.P. Kainz B. Glocker B. Rueckert D.
author_sort	Oktay O.
title	Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
title_short	Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
title_full	Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
title_fullStr	Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
title_full_unstemmed	Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
title_sort	anatomically constrained neural networks (acnns): application to cardiac image enhancement and segmentation
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2019
url	http://scholarbank.nus.edu.sg/handle/10635/150626
_version_	1781791596565495808

Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

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