activity
20182020
most citeddAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

22 citations · 23 across the 5 of their papers we have counts for

collaborators

18 papers

eess.IV2020

Channel Attention Networks for Robust MR Fingerprinting Matching

Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy +5

Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varyin…

cs.CV2020

Probabilistic 3D surface reconstruction from sparse MRI information

Katarína Tóthová, Sarah Parisot, Matthew Lee +4

Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool shou…

eess.IV2020

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran +3

Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left un…

eess.IV2020

Quality-aware semi-supervised learning for CMR segmentation

Bram Ruijsink, Esther Puyol-Anton, Ye Li +4

One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of annotated training data. To overcome this limitation, data augmentati…

eess.IV2020

Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI

Ana Lourenço, Eric Kerfoot, Connor Dibblin +7

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterised by a rapid and irregular electrical activation of the atria. Treatments for AF are often ine…

eess.IV2020

A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI

Nick Byrne, James R. Clough, Giovanni Montana +1

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 obser…