Medical Image Analysis
Analysis of medical images is essential in modern medicine. With the 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 images. 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 surgery.
Our mission is to advance the state of the art in machine learning and marry it with medical expertise, with application in computer-aided diagnosis, personalized medicine and efficient data management. Some of this technology is now incorporated within the Microsoft Amalga Unified Intelligence System.
Here is an overview of the InnerEye project
Demo videos
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Automatic 3D delineation of highly aggressive brain tumours
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Automatic localization and identification of vertebrae in 3D CT scans
...in preparation...
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Automatic localization of anatomical structures
Automatic semantic parsing of 3D medical scans
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Interactive segmentation of n-dimensional medical scans
Interactive segmentation of 3D anatomical structures
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Efficient volumetric rendering in the cloud
Efficient volumetric rendering of medical images on the GPU
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Touch-less interaction for medical imaging
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Cloud-based structured image search
Research in collaboration with the University of Oxford.
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 a radio-oncologist collaborator:
Products
Some of our technology is being transferred into Microsoft Amalga Unified Intelligence System.
Research data
We are working hard to make available some of our annotated research data for everyone to use (for non-commercial purposes only). Watch this space.
Press buzz
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People
Publications
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
- 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
- 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
- Ender Konukoglu, Ben Glocker, Darko Zikic, and Antonio Criminisi, Neighborhood Approximation Forests, in MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, 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
- 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
- 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
- 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
- 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
- Rose Johnson, Kenton O'Hara, Abigail Sellen, Claire Cousins, and Antonio Criminisi, Exploring the Potential for Touchless Interaction in Image Guided Interventional Radiology, in ACM Conference on Computer-Human Interaction (CHI). Honourable Mention Award, ACM Conference on Computer-Human Interaction, 7 May 2011
- B. Menze, G. Langs, Z. Tu, and A. Criminisi, Medical Computer Vision: recognition techniques and applications in medical imaging, Springer Verlag, February 2011
- S. Pathak, A. Criminisi, S. White, I. Munasinghe, B. Sparks, D. Robertson, and K. Siddiqui, Automatic Semantic Annotation and Validation of Anatomy in DICOM CT Images, in SPIE Medical Imaging, February 2011
- E. Konukoglu, A. Criminisi, S. Pathak, D. Robertson, S. White, D. Haynor, and K. Siddiqui, Robust Linear Registration of CT Images using Random Regression Forests, in SPIE Medical Imaging, February 2011
- Erik Pernod, Maxime Sermesant, Ender Konukoglu, Jatin Relan, Hervé Delingette, and Nicholas Ayache, A Multi-Front Eikonal model of Cardiac Electrophysiology for Interactive Simulation of Radio-Frequency Ablation, in Computers and Graphics, vol. in press, 2011
- E. Konukoglu, J. Relan, U. Cilingir, B.H. Menze, P. Chinchapatnam, A. Jadidi, H. Cochet, M. Hocini, H. Delingette, P. Jais, M. Haissaguerre, N. Ayache, and M. Sermesant, Probabilisthc Model Personalization using an Efficient Framework: Application to Eikonal-Diffusion Models in Cardiac Electrophysiology, in Prog. Biophys. Mol. Biol., 2011
- E. Geremia, O. Clatz, B. H. Menze, E. Konukoglu, A. Criminisi, and N. Ayache, Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel Magnetic Resonance, in NeuroImage, 2011
- H. Yoshida, Y. Wu, W. Cai, J. J. Nappi, M. Hirano, and A. Criminisi, Mobile Cloud-Computer-aided Diagnosis System for Colon Cancer Screening with Motion-based Virtual Colonoscopic Navigation, in Radiological Society of North America (RSNA), 2011
- B H Menze, E Stretton, E Konukoglu, and N. Ayache, Image-based modeling of tumor growth in patients with glioma., in Optimal control in image processing, Springer, Heidelberg/Germany, 2011
2010
- A. Criminisi, J. Shotton, D. Robertson, and E. Konukoglu, Regression Forests for Efficient Anatomy Detection and Localization in CT Studies, in Medical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging, MICCAI workshop, Springer Verlag, September 2010
