28 citations · 54 across the 9 of their papers we have counts for
10 papers · 1 filter
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images
Guy Leroy, Daniel Rueckert, Amir Alansary
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) syste…
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
Vitalis Vosylius, Andy Wang, Cemlyn Waters +8
Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the po…
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas +2
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a te…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li, Bishesh Khanal, Benjamin Hou +8
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring…
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
Yuanwei Li, Amir Alansary, Juan J. Cerrolaza +7
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn t…