Author Info
Name
Affiliation
Papers
MATTHEW SINCLAIR
Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
21
Collaborators
Citations 
PageRank 
81
44
8.23
Referers 
Referees 
References 
206
515
115
Search Limit
100515
Title
Citations
PageRank
Year
Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks00.342022
CAS-Net - Conditional Atlas Generation and Brain Segmentation for Fetal MRI.00.342021
Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers.20.382019
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.20.362019
Weakly Supervised Estimation of Shadow Confidence Maps in Ultrasound Imaging.00.342018
Automatic Left Ventricular Outflow Tract Classification For Accurate Cardiac Mr Planning20.422018
Deep Learning With Ultrasound Physics For Fetal Skull Segmentation00.342018
A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks.00.342018
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network.10.342018
Weakly Supervised Localisation For Fetal Ultrasound Images00.342018
Fully Automated Myocardial Strain Estimation From Cine Mri Using Convolutional Neural Networks10.352018
Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients.20.402018
Fetal Skull Reconstruction via Deep Convolutional Autoencoders.00.342018
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.10.482018
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks.10.362018
3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks.20.402018
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation.80.492017
Fully Automated Segmentation-Based Respiratory Motion Correction of Multiplanar Cardiac Magnetic Resonance Images for Large-Scale Datasets.40.462017
Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation.170.702017
A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.10.392017
Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas.00.342016