Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients

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.

Image Classification

A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN

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.

Computed Tomography (CT) Data Augmentation +2

A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images

no code implementations • 7 Mar 2024 • Cristiana Tiago, Sten Roar Snare, Jurica Sprem, Kristin McLeod

The proposed framework relies on an adversarial Denoising Diffusion Model (DDM) to synthesize echocardiography images and perform domain translation.

Denoising SSIM +1