Search Results for author: Pablo Lamata

Found 6 papers, 0 papers with code

A Generative Adversarial Model for Right Ventricle Segmentation

no code implementations27 Sep 2018 Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana

The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function.

Right Ventricle Segmentation

Automated segmentation on the entire cardiac cycle using a deep learning work-flow

no code implementations31 Aug 2018 Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana

Our initial experiments suggest that significant improvement in performance can potentially be achieved by using a recurrent neural network component that explicitly learns cardiac motion patterns whilst performing LV segmentation.

V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

no code implementations6 Aug 2018 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.

Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound

no code implementations11 Jul 2018 Nicolo' Savioli, Silvia Visentin, Erich Cosmi, Enrico Grisan, Pablo Lamata, Giovanni Montana

The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis.

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

no code implementations13 Aug 2016 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 of the left-ventricle.

Cardiac Segmentation

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