Research in Machine Learning for Medical Applications

Table of Content

Abstract

Machine learning offers methods for automatic learning of complex patterns from empirical data and making intelligent decisions based on learned behavior. This becomes often necessary in the area of medicine, where the large dimensionality of data and highly variable environments make analytical solutions impossible and human-generated, rule-based heuristics intractable. At CAMP, we develop and adapt state-of-the-art machine learning methods for medical applications. Applications include the learning of similarities across different image modalities for complex registration tasks, medical image understanding, organ localization, learning of anatomical shape, tissue classification and computer-aided diagnosis.

Research interests

• Similarity, metric, distance learning
• Computer aided diagnosis
• Pattern recognition and analysis
• Medical image understanding
• Tissue classication

Contact Person and Group Coordination

Diana Mateus	Olivier Pauly	Nassir Navab

Research Projects in Machine Learning for Medical Applications

Shape Guided Segmentation of Cardiac Boundaries Prior 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.

Non Invasive Histology of Atherosclerotic Plaque Stroke 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.

Organ Recognition Automatic 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.

Similarity/Metric/Distance Learning for Medical Applications Many 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.

Related Publications

2012
	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)
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)
	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)
	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)
	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)
	S. Atasoy, D. Mateus, A. Meining, G. Z. Yang, N. Navab Endoscopic Video Manifolds for Targeted Optical Biopsy IEEE Transactions on Medical Imaging. (bib)
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)
	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)
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)
	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)
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)

Working Group

Abouzar Eslami

Diana C. Mateus

Olivier Pauly

Loic Peter