13 citations · 23 across the 8 of their papers we have counts for
9 papers
Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
David Stojanovski, Mariana da Silva, Pablo Lamata +2
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propos…
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
David Stojanovski, Uxio Hermida, Pablo Lamata +2
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show th…
Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views
David Stojanovski, Uxio Hermida, Marica Muffoletto +3
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is t…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
A Generative Adversarial Model for Right Ventricle Segmentation
Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata +1
The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function. This work addresses the…
V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation
Nicoló Savioli, Giovanni Montana, Pablo Lamata
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable…
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata +1
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmen…
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
Nicolo' Savioli, Silvia Visentin, Erich Cosmi +3
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task…
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
Rudra P K Poudel, Pablo Lamata, Giovanni Montana
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images…