Medical Image Analysis
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Our mission. To advance the state of the art in efficient machine learning and computer vision, and marry them with medical expertise to help with computer-aided diagnosis, personalized treatment and efficient data management. |
Brief project description. Analysis of medical images is essential in modern medicine. With the ever increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment and monitoring.
The InnerEye research project focuses on the automatic analysis of patients' medical scans. It uses state of the art machine learning techniques for the:
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automatic delineation and measurement of healthy anatomy and anomalies;
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robust registration for monitoring disease progression;
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semantic navigation and visualization for improved clinical workflow;
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development of natural user interfaces for medical practitioners.
Some recent achievements. In Sep 2012 our algorithm for the automatic detection and localization of anatomy within Computed Tomography scans has obtained FDA approval. Some of this technology is now incorporated within the Caradigm's Amalga System.
- An overview of the InnerEye project
Demo videos
- Automatic 3D delineation of highly aggressive brain tumours
- Automatic localization and identification of vertebrae in 3D CT scans
- Kinect-based touchless interaction for surgery
- Automatic anatomy localization in 3D medical images
- Interactive segmentation of structures in 3D medical images
- Cloud-based volumetric rendering of medical images
Free research data download
We are working hard to make available some of our annotated research data for everyone to use (for non-commercial purposes only). Please watch this space.
Free research tools download
Please download freely available software tools from here.
Our scientific collaborations
Current collaborators include: Johns Hopkins Medical Institute, The Universty of Oxford, Cornell Medical School, Massachusetts General Hospital, University of Washington, Kings College London, INRIA Asclepios and Addenbrooke's NHS Hospital in Cambridge, amongst others.
Our work described by one of our radio-oncologist collaborators:
Some press buzz
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People
Our scientific publications
2013
- P. Kontschieder, P. Kohli, J. Shotton, and A. Criminisi, GeoF: Geodesic Forests for Learning Coupled Predictors, in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, June 2013
- A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, Springer, February 2013
- Ender Konukoglu, Ben Glocker, Antonio Criminisi, and Kilian M. Pohl, WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity, in Transactions Pattern Analysis and Machine Intelligence (PAMI), IEEE, 2013
- A. Criminisi, D. Robertson, E. Konukoglu, J. Shotton, S. Pathak, S. White, and K. Siddiqui, Regression Forests for Efficient Anatomy Detection and Localization in Computed Tomography Scans, in Medical Image Analysis (MedIA), Elsevier, 2013
2012
- P. Kontschieder, S. Rota Bulo', A. Criminisi, P. Kohli, M. Pelillo, and H. Bischof, Context-Sensitive Decision Forests for Object Detection, in Neural Information Processing Systems (NIPS), December 2012
- Ender Konukoglu, Ben Glocker, DongHye Ye, Antonio Criminisi, and Kilian M. Pohl, Discriminative Segmentation-based Evaluation through Shape Dissimilarity, in Transactions on Medical Imaging (TMI), IEEE, December 2012
- D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, J. Shotton, C. Demiralp, O. Thomas, T. Das, R. Jena, and S. Price, Decision Forests for Tissue-specific Segmentation of High-grade Gliomas in Multi-channel MR, in MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, October 2012
- L. Le Folgoc, H. Delingette, N. Ayache, and A. Criminisi, Current-based 4D shape analysis for the mechanical personalization of heart models, in MICCAI workshop on Medical Computer Vision (MCV), October 2012
- Ben Glocker, Johannes Feulner, Antonio Criminisi, David R. Haynor, and Ender Konukoglu, Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans, in MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, October 2012
- Ben Glocker, Olivier Pauly, Ender Konukoglu, and Antonio Criminisi, Joint Classification-Regression Forests for Spatially Structured Multi-Object Segmentation, in ECCV - 12th European Conference on Computer Vision, Springer, October 2012
- Ender Konukoglu, Ben Glocker, Darko Zikic, and Antonio Criminisi, Neighbourhood Approximation Forests, in MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, October 2012
- K. Simonyan, M. Modat, S. Ourselin, A. Criminisi, and A. Zisserman, Immediate ROI Search for 3D Medical Images, in MICCAI workshop on Medical Content-based Retrieval for Clinical Decision Support (MCBR-CDS), October 2012
- Elena Bernardis, Ender Konukoglu, Yangming Ou, Dimitris Metaxas, Benoit Desjardins, and Killian M. Pohl, Temporal Shape Analysis via the Spectral Signature, in MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, October 2012
- E. Geremia, B. Menze, M. Prastawa, A. Criminisi, and N. Ayache, Brain tumor cell rate estimation from multi-modal MR images based on a synthetic tumor growth model, in MICCAI workshop on Medical Computer Vision (MCV), October 2012
- Antonio Criminisi, Jamie Shotton, and Ender Konukoglu, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, in Foundations and Trends® in Computer Graphics and Vision: Vol. 7: No 2-3, pp 81-227, NOW Publishers, 2012
- Sayan D. Pathak, Woojin Kim, Indeera Munasinghe, Antonio Criminisi, Steve White, and Khan Siddiqui, Linking DICOM Pixel Data with Radiology Reports Using Automatic Semantic Annotation, in SPIE Medical Imaging, SPIE, 2012
- Duncan Robertson, Sayan D. Pathak, Antonio Criminisi, Steve White, David Haynor, Oliver Chen, and Khan Siddiqui, Comparative Analysis of Semantic Localization Accuracies Between Adult and Pediatric DICOM CT Images, in SPIE Medical Imaging, SPIE, 2012
2011
- A. Criminisi, J. Shotton, and E. Konukoglu, Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, no. MSR-TR-2011-114, 28 October 2011
- O. Pauly, B. Glocker, A. Criminisi, D. Mateus, A. Martinez Moller, S. Nekolla, and N. Navab, Fast Multiple Organs Detection and Localization in Whole-Body MR Dixon Sequences, in MICCAI - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, September 2011
- A. Criminisi, K. Juluru, and S. Pathak, A Discriminative-Generative Model for Detecting Intravenous Contrast in CT Images, in MICCAI, September 2011
- K. Simonyan, A. Criminisi, and A. Zisserman, Immediate Structured Image Search for Medical Images, in MICCAI, September 2011
- J. Margeta, E. Geremia, A. Criminisi, and N. Ayache, Layered Spatio-Temporal Forests for Left Ventricle Segmentation from 4D Cardiac MRI Data, in MICCAI workshop: Statistical Atlases and Computational Models of the Heart (STACOM), September 2011
- J. E. Iglesias, E. Konukoglu, A. Montillo, Z. Tu, and A. Criminisi, Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active learning, in Information Processing in Medical Imaging (IPMI), Springer Verlag, July 2011
- A. Montillo, J. Shotton, J. Winn, J. E. Iglesias, D. Metaxas, and A. Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, in Information Processing in Medical Imaging (IPMI), July 2011
- Bjoern H. Menze, Koen Van Leemput, Antti Honkela, Ender Konukoglu, Marc-Andre Weber, Nicholas Ayache, and Polina Golland, A Generative Approach for Image-Based Modeling of Tumor Growth, in Information Processing in Medical Imaging (IPMI), Springer Verlag, July 2011
