Imperial College London

Dr Ben Glocker

Faculty of EngineeringDepartment of Computing

Reader in Machine Learning for Imaging



+44 (0)20 7594 8334b.glocker Website CV




377Huxley BuildingSouth Kensington Campus





I am Reader in Machine Learning for Imaging co-leading the Biomedical Image Analysis Group. I am also Adviser – Medical Image Analysis at HeartFlow and I am leading the London-based HeartFlow-Imperial Research Team. I work as scientific adviser for Definiens and Kheiron Medical Technologies.

My research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving image-based detection and diagnosis of disease.

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Selected Publications

Journal Articles

Lee M, Petersen K, Pawlowski N, et al., 2019, TeTrIS: template transformer networks for image segmentation with shape priors, IEEE Transactions on Medical Imaging, Vol:38, ISSN:0278-0062, Pages:2596-2606

Schlemper J, Oktay O, Schaap M, et al., 2019, Attention gated networks: Learning to leverage salient regions in medical images., Med Image Anal, Vol:53, Pages:197-207

Robinson R, Valindria VV, Bai W, et al., 2019, Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study, Journal of Cardiovascular Magnetic Resonance, Vol:21, ISSN:1097-6647

Bai W, Sinclair M, Tarroni G, et al., Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Parisot S, Ktena SI, Ferrante E, et al., 2018, Disease prediction using graph convolutional networks: application to Autism Spectrum Disorder and Alzheimer's disease, Medical Image Analysis, Vol:48, ISSN:1361-8415, Pages:117-130

Ktena SI, Parisot S, Ferrante E, et al., 2017, Metric learning with spectral graph convolutions on brain connectivity networks., Neuroimage, Vol:169, ISSN:1053-8119, Pages:431-442

Oktay O, Ferrante E, Kamnitsas K, et al., 2017, Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation, IEEE Transactions on Medical Imaging, Vol:37, ISSN:0278-0062, Pages:384-395

Parisot S, Glocker B, Ktena SI, et al., 2017, A flexible graphical model for multi-modal parcellation of the cortex., Neuroimage, Vol:162, ISSN:1053-8119, Pages:226-248

Valindria V, Lavdas I, Bai W, et al., 2017, Reverse classification accuracy: predicting segmentation performance in the absence of ground truth, IEEE Transactions on Medical Imaging, Vol:36, ISSN:1558-254X, Pages:1597-1606

Konukoglu E, Glocker B, 2017, Reconstructing Subject-Specific Effect Maps, Neuroimage, ISSN:1053-8119

Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2016, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, Vol:36, ISSN:1361-8423, Pages:61-78

Rueckert D, Glocker B, Kainz B, 2016, Learning clinically useful information from images: Past, present and future, Medical Image Analysis, Vol:33, ISSN:1361-8423, Pages:13-18

Glocker B, Shotton J, Criminisi A, et al., 2014, Real-Time RGB-D Camera Relocalization via Randomized Ferns for Keyframe Encoding, Transactions on Visualization and Computer Graphics

Zikic D, Glocker B, Criminisi A, 2014, Encoding atlases by randomized classification forests for efficient multi-atlas label propagation, Medical Image Analysis, Vol:18, ISSN:1361-8423, Pages:1262-1273

Konukoglu E, Glocker B, Zikic D, et al., 2013, Neighbourhood approximation using randomized forests., Medical Image Analysis

Glocker B, Sotiras A, Komodakis N, et al., 2011, Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods, Annual Review of Biomedical Engineering, Vol:13, ISSN:1523-9829, Pages:219-244

Glocker B, Komodakis N, Tziritas G, et al., 2008, Dense image registration through MRFs and efficient linear programming., Medical Image Analysis, Vol:12, Pages:731-741

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