Imperial College London

Dr Ben Glocker

Faculty of EngineeringDepartment of Computing

Senior Lecturer
 
 
 
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Contact

 

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

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Summary

I am Senior Lecturer in Medical Image Computing and one of three academics 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 diagnosis, therapy and intervention.


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Selected Publications

Journal Articles

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

Robinson EC, Garcia K, Glasser MF, et al., 2018, Multimodal surface matching with higher-order smoothness constraints, Neuroimage, Vol:167, ISSN:1053-8119, Pages:453-465

Hou B, Khanal B, Alansary A, et al., 2018, 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images, IEEE Transactions on Medical Imaging, Vol:37, ISSN:0278-0062, Pages:1737-1750

Ktena SI, Parisot S, Ferrante E, et al., 2018, Metric learning with spectral graph convolutions on brain connectivity networks, Neuroimage, Vol:169, ISSN:1053-8119, Pages:431-442

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

Oktay O, Ferrante E, Kamnitsas K, et al., 2018, Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation, IEEE Transactions on Medical Imaging, Vol:37, ISSN:0278-0062, Pages:384-395

Valindria VV, 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:0278-0062, Pages:1597-1606

Rueckert D, Glocker B, Kainz B, 2016, Learning clinically useful information from images: Past, present and future, Medical Image Analysis, Vol:33, ISSN:1361-8415, Pages:13-18

Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2017, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, Vol:36, ISSN:1361-8415, Pages:61-78

Glocker B, Shotton J, Criminisi A, et al., 2015, Real-Time RGB-D Camera Relocalization via Randomized Ferns for Keyframe Encoding, IEEE Transactions on Visualization and Computer Graphics, Vol:21, ISSN:1077-2626, Pages:571-583

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:1361-8415, Pages:1262-1273

Konukoglu E, Glocker B, Zikic D, et al., 2013, Neighbourhood approximation using randomized forests, Medical Image Analysis, Vol:17, ISSN:1361-8415, Pages:790-804

Glocker B, Komodakis N, Tziritas G, et al., 2008, Dense image registration through MRFs and efficient linear programming, Medical Image Analysis, Vol:12, ISSN:1361-8415, Pages:731-741

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

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