no code implementations • 17 May 2022 • Liu Li, Qiang Ma, Matthew Sinclair, Antonios Makropoulos, Joseph Hajnal, A. David Edwards, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development.
no code implementations • 27 Feb 2022 • George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach
This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications.
1 code implementation • 16 Feb 2022 • Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.
no code implementations • 2 Dec 2021 • Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González, Matei Grama, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary
The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients.
1 code implementation • 6 Sep 2021 • Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).
no code implementations • 31 Jul 2021 • Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Fahdi Kanavati, Arjun Nair, Julia Schnabel, Ben Glocker
This mismatch is known as sampling bias.
no code implementations • 17 Sep 2020 • Matei Grama, Maria Musat, Luis Muñoz-González, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary
In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.
Cryptography and Security
1 code implementation • 18 Aug 2020 • Guy Leroy, Daniel Rueckert, Amir Alansary
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
1 code implementation • 13 Aug 2020 • Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary
In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.
1 code implementation • MIDL 2019 • Ahmed E. Fetit, Amir Alansary, Lucilio Cordero-Grande, John Cupitt, Alice B. Davidson, A. David Edwards, Joseph V. Hajnal, Emer Hughes, Konstantinos Kamnitsas, Vanessa Kyriakopoulou, Antonios Makropoulos, Prachi A. Patkee, Anthony N. Price, Mary A. Rutherford, Daniel Rueckert
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
no code implementations • 29 Aug 2019 • Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.
1 code implementation • 30 Jun 2019 • Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas, Daniel Rueckert, Bernhard Kainz
We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.
1 code implementation • 19 Jun 2018 • Yuanwei Li, Bishesh Khanal, Benjamin Hou, Amir Alansary, Juan J. Cerrolaza, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
1 code implementation • 18 Jun 2018 • Yuanwei Li, Amir Alansary, Juan J. Cerrolaza, Bishesh Khanal, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert
PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.
no code implementations • 8 Jun 2018 • Amir Alansary, Loic Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
1 code implementation • 2 May 2018 • Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven McDonagh, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
no code implementations • 19 Sep 2017 • Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.
1 code implementation • 28 Feb 2017 • Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.
no code implementations • 28 Feb 2017 • Steven McDonagh, Benjamin Hou, Konstantinos Kamnitsas, Ozan Oktay, Amir Alansary, Mary Rutherford, Jo V. Hajnal, Bernhard Kainz
Fast imaging is required for targets that move to avoid motion artefacts.
1 code implementation • 22 Nov 2016 • Amir Alansary, Bernhard Kainz, Martin Rajchl, Maria Murgasova, Mellisa Damodaram, David F. A. Lloyd, Alice Davidson, Steven G. McDonagh, Mary Rutherford, Joseph V. Hajnal, Daniel Rueckert
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus.
no code implementations • 3 Jun 2016 • Martin Rajchl, Matthew C. H. Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, Daniel Rueckert
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods.