Titre : Making Sense of the Data Trove Hidden in Medical Ultrasound
Conférencier : Hassan Rivaz
Résumé : This talk focuses on developing image analysis techniques that reveal otherwise hidden information in clinical ultrasound data. Ultrasound is one of the most commonly used imaging modalities because of its low cost and ease of use. However, it has two main drawbacks. First, raw ultrasound data is not suitable for visualization, and as such, is converted to the familiar grey-scale images which leads to a loss of most of its information. Second, these grey-scale images are hard to interpret since they are noisy and collected at oblique angles. In this talk, we tackle these issues by developing techniques that extract clinically useful information such as tissue elasticity from the complex raw ultrasound signals, and register them to other modalities such as Magnetic Resonance Imaging (MRI) to help with their interpretation.
Biographie : Hassan Rivaz is an Assistant Professor in Electrical and Computer Engineering and a Concordia University Research Chair in Medical Image Analysis. He is an Associate Editor of IEEE Transactions on Medical Imaging (TMI), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (TUFFC). He served as an Area Chair of MICCAI 2017 and MICCAI 2018 conferences and a co-organized CuRIOUS MICCAI 2018 Challenge on correction of brain shift using ultrasound, and CereVis MICCAI 2018 Workshop on Cerebral Data Visualization. He also co-organized the elastography tutorial at IEEE ISBI 2018, and will co-organize the same tutorial at ISBI 2019. He directs the IMPACT lab: IMage Processing and Characterization of Tissue, which can be found at http://sonography.ai
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