Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Ultrasound Based Tissue Characterization

Ultrasound Based Tissue Characterization

In medical collaboration with:
Stephane Carlier, Andreas Koenig and Adnan Kastrati,

Contact Person(s): Amin Katouzian ,Nassir Navab,

Keywords: Segmentation, Medical Imaging, Ultrasound, Machine Learning for Medical Applications


clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode ultrasound image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess lesion stage. We propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated.

Clinical Relevance

Interventional Cardiology, Oncology.


Contact Person(s)

Amin Katouzian
Dr. Amin Katouzian
Nassir Navab
Prof. Dr. Nassir Navab

Working Group

Amin Katouzian
Dr. Amin Katouzian
Debdoot Sheet
Debdoot Sheet
Sailesh Conjeti
Dr. Sailesh Conjeti


Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.

internal project page

Please contact Amin Katouzian ,Nassir Navab, for available student projects within this research project.

Edit | Attach | Refresh | Diffs | More | Revision r1.2 - 15 Mar 2015 - 11:24 - SebastianPoelsterl

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