no code implementations • 21 Aug 2020 • Nick Byrne, James R. Clough, Giovanni Montana, Andrew P. King
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation.
no code implementations • 24 Jun 2020 • Esther Puyol-Antón, Chen Chen, James R. Clough, Bram Ruijsink, Baldeep S. Sidhu, Justin Gould, Bradley Porter, Mark Elliott, Vishal Mehta, Daniel Rueckert, Christopher A. Rinaldi, Andrew P. King
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
no code implementations • 11 Oct 2019 • Ilkay Oksuz, James R. Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel
In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.
1 code implementation • 4 Oct 2019 • James R. Clough, Nicholas Byrne, Ilkay Oksuz, Veronika A. Zimmer, Julia A. Schnabel, Andrew P. King
We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient.
1 code implementation • 24 Sep 2019 • Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
no code implementations • 28 Aug 2019 • Tong Zhang, Laurence H. Jackson, Alena Uus, James R. Clough, Lisa Story, Mary A. Rutherford, Joseph V. Hajnal, Maria Deprez
The results show that the proposed pipeline can accurately estimate the respiratory state and reconstruct 4D SR volumes with better or similar performance to the 3D SVR pipeline with less than 20\% sparsely selected slices.
no code implementations • 23 Aug 2019 • Nick Byrne, James R. Clough, Isra Valverde, Giovanni Montana, Andrew P. King
In a series of five-fold cross-validations, we demonstrate the performance gain produced by this pipeline and the relevance of topological considerations to the segmentation of congenital heart defects.
no code implementations • 13 Aug 2019 • Esther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Oksuz, Daniel Rueckert, Reza Razavi, Andrew P. King
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.
no code implementations • 14 Jun 2019 • James R. Clough, Ilkay Oksuz, Esther Puyol-Anton, Bram Ruijsink, Andrew P. King, Julia A. Schnabel
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.
no code implementations • 17 May 2019 • Alberto Gomez, Cornelia Schmitz, Markus Henningsson, James Housden, Yohan Noh, Veronika A. Zimmer, James R. Clough, Ilkay Oksuz, Nicolas Toussaint, Andrew P. King, Julia A. Schnabel
Motion imaging phantoms are expensive, bulky and difficult to transport and set-up.
no code implementations • 29 Jan 2019 • James R. Clough, Ilkay Oksuz, Nicholas Byrne, Julia A. Schnabel, Andrew P. King
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so.
no code implementations • ICLR 2018 • Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth
Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.
no code implementations • 30 Oct 2013 • James R. Clough, Jamie Gollings, Tamar V. Loach, Tim S. Evans
In many complex networks the vertices are ordered in time, and edges represent causal connections.
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