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Giacomo Tarroni
Researcher at Imperial College London
Publications - 71
Citations - 2414
Giacomo Tarroni is an academic researcher from Imperial College London. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 16, co-authored 67 publications receiving 1356 citations. Previous affiliations of Giacomo Tarroni include University of Chicago & University of Padua.
Papers
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Journal ArticleDOI
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Wenjia Bai,Matthew Sinclair,Giacomo Tarroni,Ozan Oktay,Martin Rajchl,Ghislain Vaillant,Aaron M. Lee,Nay Aung,Elena Lukaschuk,Mihir M. Sanghvi,Filip Zemrak,Kenneth Fung,José Miguel Paiva,Valentina Carapella,Young Jin Kim,Hideaki Suzuki,Bernhard Kainz,Paul M. Matthews,Steffen E. Petersen,Stefan K. Piechnik,Stefan Neubauer,Ben Glocker,Daniel Rueckert +22 more
TL;DR: An automated analysis method based on a fully convolutional network achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.
Book ChapterDOI
Semi-supervised learning for network-based cardiac MR image segmentation
Wenjia Bai,Ozan Oktay,Matthew Sinclair,Hideaki Suzuki,Martin Rajchl,Giacomo Tarroni,Ben Glocker,Andrew P. King,Paul M. Matthews,Daniel Rueckert +9 more
TL;DR: A semi-supervised learning approach, in which a segmentation network is trained from both labelled and unlabelled data, which outperforms a state-of-the-art multi-atlas segmentation method by a large margin and the speed is substantially faster.
Journal ArticleDOI
Deep learning for cardiac image segmentation: A review
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.
Journal ArticleDOI
Deep Learning for Cardiac Image Segmentation: A Review.
Chen Chen,Chen Qin,Huaqi Qiu,Giacomo Tarroni,Giacomo Tarroni,Jinming Duan,Wenjia Bai,Daniel Rueckert +7 more
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria and vessels).
Journal ArticleDOI
A population-based phenome-wide association study of cardiac and aortic structure and function
Wenjia Bai,Hideaki Suzuki,Hideaki Suzuki,Jian Huang,Catherine Francis,Shuo Wang,Giacomo Tarroni,Giacomo Tarroni,Florian Guitton,Nay Aung,Kenneth Fung,Steffen E. Petersen,Stefan K. Piechnik,Stefan Neubauer,Evangelos Evangelou,Evangelos Evangelou,Abbas Dehghan,Declan P. O'Regan,Martin R. Wilkins,Yike Guo,Paul M. Matthews,Daniel Rueckert +21 more
TL;DR: This study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.