13 citations · 31 across the 11 of their papers we have counts for
11 papers
TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors
Nicolò Savioli, Luca Del Tongo
Every new clinical imaging device creates a domain shift where dense gland masks are expensive yet cheap clinical signals -- eyelid outlines, Pult grades, morphometric ratios -- ar…
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Nicolo Savioli, Antonio de Marvao, Wenjia Bai +5
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion. However, techniques such as cardiac magnetic resonance imaging a…
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
Shuo Wang, Chen Qin, Nicolo Savioli +6
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the…
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…
One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep siamese network
Nicolò Savioli
The novel coronavirus (2019-nCoV) has been declared to be a new international health emergence and no specific drug has been yet identified. Several methods are currently being eva…
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
Shihao Jin, Nicolò Savioli, Antonio de Marvao +4
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clin…
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression
Nicoló Savioli
Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recentl…
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…