no code implementations • 15 Mar 2024 • Müjde Akdeniz, Claudia Alessandra Manetti, Tijmen Koopsen, Hani Nozari Mirar, Sten Roar Snare, Svein Arne Aase, Joost Lumens, Jurica Šprem, Kristin Sarah McLeod
In this work, we propose a single framework to predict myocardial disease substrates at global, territorial, and segmental levels using regional myocardial strain traces as input to a convolutional neural network (CNN)-based classification algorithm.
no code implementations • 15 Mar 2024 • Benjamin Strandli Fermann, John Nyberg, Espen W. Remme, Jahn Frederik Grue, Helén Grue, Roger Håland, Lasse Lovstakken, Håvard Dalen, Bjørnar Grenne, Svein Arne Aase, Sten Roar Snar, Andreas Østvik
On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1. 8 (30 ms).
no code implementations • 8 Mar 2024 • Cristiana Tiago, Andrew Gilbert, Ahmed S. Beela, Svein Arne Aase, Sten Roar Snare, Jurica Sprem
A quantitative analysis of the 3D segmentations given by the models trained with the synthetic images indicated the potential use of this GAN approach to generate 3D synthetic data, use the data to train DL models for different clinical tasks, and therefore tackle the problem of scarcity of 3D labeled echocardiography datasets.
no code implementations • 29 Feb 2024 • Sarina Thomas, Cristiana Tiago, Børge Solli Andreassen, Svein Arne Aase, Jurica Šprem, Erik Steen, Anne Solberg, Guy Ben-Yosef
Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures.
no code implementations • 6 Nov 2019 • Andrew Gilbert, Marit Holden, Line Eikvil, Mariia Rakhmail, Aleksandar Babic, Svein Arne Aase, Eigil Samset, Kristin McLeod
We analyze example images that fall outside of our proposed classes to show our confidence metric can prevent many misclassifications.
no code implementations • 6 Nov 2019 • Andrew Gilbert, Marit Holden, Line Eikvil, Svein Arne Aase, Eigil Samset, Kristin McLeod
Treating the problem as a landmark detection problem, we propose a modified U-Net CNN architecture to generate heatmaps of likely coordinate locations.