In medical collaboration with: |
Stephane Carlier, Andreas Koenig and Adnan Kastrati,
Contact Person(s): Amin Katouzian ,Nassir Navab,
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.
Interventional Cardiology, Oncology.
Visit our lab at Garching.
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Please contact Amin Katouzian
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