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Diana Mateus |
Olivier Pauly |
Nassir Navab |
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Shape Guided Segmentation of Cardiac BoundariesPrior shape information has been shown to be invaluable for segmenting cardiac boundaries. We develop new methods of exploiting such prior information to guide the segmentation by using techniques of machine learning or formulating the segmentation problem to fit our requirements in segmentation of 4D cardiac data. |
patient-Specific Translational research on Atherosclerosis and Diagnosis (STAnD) |
Non Invasive Histology of Atherosclerotic PlaqueStroke is the third leading cause of death in Germany. It is a neurology injury, whereby the oxygen supply to parts of the brain gets cut off. About 80% of these strokes are due to ischemia, i.e. an occlusion of a blood vessel leading to an interrupted blood flow. Stenosis inside the carotid artery imaged using four different MR weightings Special setting in this project is the arteria carotis. Plaque is most likely to develop at the branching of the arteria carotis communis into the arteria carotis interna (leading to the brain) and the arteria carotis externa. This can lead to an abnormal narrowing, called a stenosis. According to the American Heart Association these plaques can be divided into different types, based on their consistency and structure. Until now the decision about a surgery was only based on the degree of the stenosis and not on the type of plaque causing it. This is a faulty approach since there is a plaque type (Type IV) which constitutes a relevant clinical danger, although it does not necessary come along with a stenosis. Unlike most other image modalities MR images do not only give information about the degree of the stenosis, but also about the consistency of the plaque. Using different weighted MR images it is possible to correctly classify plaque into the types defined by the AHA. The main goal of this project is to create a classification tool based on T1, T2, Proton Density and 'Time of flight' weighted images. To achieve this goal the arteria carotis and the plaque have to be segmented from the images. Furthermore various features of the plaque have to be extracted in order to get information needed for the classification. |
Decision Support System for Treating Patients with Coronary Artery DiseaseEfficacious treatment and management of patient with the Coronary Artery Disease (CAD) has drawn a lot of attention especially because of the high prevalence and high mortality rate. Two major treatment options are Coronary Artery Bypass Grafting (CABG) and Percutaneous Coronary Intervention (PCI). CABG is more expensive and invasive, however, the overall prognosis is better than PCI, which is widely considered as a less expensive and minimally-invasive option. It is documented that a large percentage of patients (up to one third) undergoing PCI procedure require repeat procedure after several years. It is beneficial to both patients and payers to identify this high-risk patient group as early as possible based on a series of in-vitro and imaging tests and possibly choose an alternate treatment option. Moreover, the identification of this high-risk patient group within the early months of PCI intervention could also trigger better management of these patients through administering possibly invasive tests (e.g., optical coherence tomography-OCT) to characterize and quantify the disease process and the associated risk. Our project partner at German Heart Center has collected large amount of data from patient cohorts that includes results of commercially available in-vitro tests, imaging tests, and for subset of patients the plasma samples and the follow-up data with clinical outcomes. The goal of this project is to identify features such as blood biomarkers that are predictive of PCI prognosis, to devise an algorithm decision support system to aggregate the biomarker test (including imaging) results and optimize the correlation to the clinical outcome, characterize and validate the system built based on retrospective data. |
Similarity/Metric/Distance Learning for Medical ApplicationsMany medical applications such as registration or tracking can be seen as the optimization of an objective function which involves a data term or similarity measure. Classical similarity measures rely for instance on image intensities, gradients or intensity statistics. In the case of noise or background clutter which is very frequent in the case of medical imaging, they might lead to registration/tracking errors. In this project, we investigate different approches and applications of learning a similarity measure directly from the data, leading to a more robust data term which is adapted to the image characteristics. |
Organ RecognitionAutomatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. In this project, we investigate hierachical regression methods based on Random Forests and Random Ferns. Such hierarchical approaches permit to subdivide efficiently the feature space and to create a partition over it. In each cell of the resulting partition, data can be easily modeled using simple mathematical models such as constant or linear. The combination of these models over the whole partition results then in a complex non-linear model. |
| 2012 | |
| B. Glocker, O. Pauly, E. Konukoglu, A. Criminisi
Joint Classification-Regression Forests for Spatially Structured Multi-Object Segmentation 12th European Conference on Computer Vision (ECCV), Firenze, Italy, October 7-13 2012. (bib) |
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| O. Pauly, A. Ahmadi, A. Plate, K. Bötzel, N. Navab
Detection of Substantia Nigra Echogenicities in 3D Transcranial Ultrasound for Early Diagnosis of Parkinson Disease In Proc. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice (FR), October 01-05, 2012 (bib) |
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| Y. Chen, T. Hrabe, S. Pfeffer, O. Pauly, D. Mateus, N. Navab, F. Foerster
Detection and Identification of Macromolecular Complexes in Cryo-Electron Tomograms Using Support Vector Machines IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2012), Barcelona, Spain, May 2 - 5, 2012 (bib) |
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| S. Atasoy, D. Mateus, A. Meining, G. Z. Yang, N. Navab
Endoscopic Video Manifolds for Targeted Optical Biopsy IEEE Transactions on Medical Imaging. (bib) |
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| 2011 | |
| O. Pauly, D. Mateus, N. Navab
STARS: A New Ensemble Partitioning Approach ICCV Workshop on Information Theory in Computer Vision and Pattern Recognition (ITINCVPR 2011), Madrid, Spain, November 2011 (bib) |
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| A. Safi, M. Baust, O. Pauly, V. Castaneda, T. Lasser, D. Mateus, N. Navab, R. Hein, M. Ziai
Computer-Aided Diagnosis of Pigmented Skin Dermoscopic Images MICCAI Workshop on Medical Content-based Retrieval for Clinical Decision Support, Toronto, Canada, September 2011 (bib) |
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| O. Pauly, D. Mateus, N. Navab
Building Implicit Dictionaries based on Extreme Random Clustering for Modality Recognition MICCAI Workshop on Medical Content-based Retrieval for Clinical Decision Support, Toronto, Canada, September 2011 (bib) |
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| O. Pauly, B. Glocker, A. Criminisi, D. Mateus, A. Martinez-Möller, S. Nekolla, N. Navab
Fast Multiple Organs Detection and Localization in Whole-Body MR Dixon Sequences To appear in Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, September 2011 (bib) |
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| 2010 | |
| O. Pauly, D. Mateus, N. Navab
ImageCLEF 2010 Working Notes on the Modality Classification Subtask. Cross Language Image Retrieval Workshop (ImageCLEF? 2010), Medical Retrieval, Padua, Italy, September 2010 (bib) |
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| O. Pauly, H. Heibel, N. Navab
A Machine Learning Approach for Deformable Guide-Wire Tracking in Fluoroscopic Sequences. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Beijing, China, September 2010 (bib) |
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| 2009 | |
| O. Pauly, N. Padoy, H. Poppert, L. Esposito, H-H. Eckstein, N. Navab
Towards Application-specific Multi-modal Similarity Measures: a Regression Approach. MICCAI Workshop on Probabilistic Models in Medical Image Analysis (PMMIA), London, UK, September 2009. (bib) |
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| A. Taki, S. Avansari, A. Roodaki, S.H. Rezatofighi, S.K. Setarehdan, N. Navab
Developing new tool for automatic analysis of IVUS images: from border detection to plaque characterization 23nd International Congress and Exhibition June 23 - 27, 2009, Berlin, Germany (bib) |
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| 2007 | |
| A.Soltanzadi, Z.Najafi, A. Roodaki, A. Taki, S.K. Setarehdan, R. Zoroofi, N. Navab
Full automatic border extraction of coronary arteries in IVUS images using deformable models 14th Conference on Medical Engineering, Tehran, Iran, Dec.2007 (bib) |
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