Publications
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
M. Rajchl, M.C.H. Lee, O. Oktay, K. Kamnitsas, J. Passerat-Palmbach, W. Bai, B. Kainz, D. Rueckert , arXiv:1605.07866, to appear in IEEE Transactions on Medical Imaging, 2016
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
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 , submitted to IEEE Transactions on Medical Imaging, preprint available on arXiv:1611.07289, 2016
Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks
Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Sandra Smith, Bernhard Kainz, Daniel Rueckert , Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II, pp.203–211, 2016
Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Sandra Smith, Bernhard Kainz, Daniel Rueckert
Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI
Amir Alansary, Konstantinos Kamnitsas, Alice Davidson, Rostislav Khlebnikov, Martin Rajchl, Christina Malamateniou, Mary Rutherford, Joseph V Hajnal, Ben Glocker, Daniel Rueckert, others , International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.589–597, 2016
Amir Alansary, Konstantinos Kamnitsas, Alice Davidson, Rostislav Khlebnikov, Martin Rajchl, Christina Malamateniou, Mary Rutherford, Joseph V Hajnal, Ben Glocker, Daniel Rueckert, others
Prenatal MRI visualisation of the aortic arch and fetal vasculature using motion-corrected slice-to-volume reconstruction
David Lloyd, Bernhard Kainz, Joshua F van Amerom, Maelene Lohezic, Kuberan Pushparajah, John M Simpson, Christina Malamateniou, Joseph V Hajnal, Mary Rutherford, Reza Razavi , Journal of Cardiovascular Magnetic Resonance, volume 18, pp.1, 2016
High-Performance Motion Correction of Fetal MRI
Bernhard Kainz, David F.A. Lloyd, Amir Alansary, Maria Kuklisova Murgasova, Rostislav Khlebnikov, Daniel Rueckert, Mary Rutherford, Reza Razavi, Jo V. Hajnal , Proc. EuroRV3: EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization, pp.5–7, 2016
Learning clinically useful information from images: Past, present and future
Daniel Rueckert, Ben Glocker, Bernhard Kainz , Medical Image Analysis , volume 33, pp.13 - 18, 2016
Daniel Rueckert, Ben Glocker, Bernhard Kainz
Abstract
Abstract Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work.Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, Ben Glocker , arXiv preprint arXiv:1603.05959, 2016
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, Ben Glocker
Learning under Distributed Weak Supervision
Martin Rajchl, Matthew CH Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, Daniel Rueckert , arXiv preprint arXiv:1606.01100, 2016
Martin Rajchl, Matthew CH Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, Daniel Rueckert
Note Special Issue on Discrete Graphical Models in Biomedical Image Analysis
Ben Glocker, Nikos Paragios, Ramin Zabih , Medical Image Analysis, volume 27, pp.1–2, 2016
Ben Glocker, Nikos Paragios, Ramin Zabih