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

Abstract

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.

Team

Contact Person(s)

Dr. Amin Katouzian

Prof. Dr. Nassir Navab

Working Group

Dr. Amin Katouzian

Debdoot Sheet

Dr. Sailesh Conjeti

Location

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.