Ieee Mb White

Michalis E. Zervakis

  • Technical University of Crete
  • Professor at the School of Electrical and Computer Engineering
  • Vice Rector at the Technical University of Crete
Presenter Bio

Michalis E. Zervakis holds a Ph.D degree from the University of Toronto, Department of Electrical Engineering. He is a Professor at the School of Electrical and Computer Engineering and Vice Rector at the Technical University of Crete. His research interests include modern aspects of signal and image processing, data mining and pattern recognition, imaging systems and integrated automation systems. Research applications include bioinformatics, biosignal analysis and medical Imaging, biomarker selection from mass genomic data, time-frequency biosignal and EEG characterization, modeling of disease state and progression, as well as cancer research on diagnosis & prognosis. He is coauthor in more than 280 scientific papers in international journals and conference proceedings. He has participated in several national and European research projects and has participated in the scientific committees of several IEEE conferences.

Medical Image Segmentation, Radiomics and Radio-transcriptomics: applications in cancer diagnosis
Early diagnosis of cancer in its initial stages, when the tumor is confined in a small area, increases the probability of survival. Screening at-risk populations, suspect for cancer development, is suggested by the doctors as a vital tool to diagnose the disease at an early stage, when the treatment has more chances to succeed. The most popular screening tests include Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and PET/CT scans. The area of ‘radiomics’ has focused on the extraction of quantitative features from medical images in order to reveal the development and progression of cancer, providing valuable information for clinical diagnosis and treatment planning. In radiomics studies imaging characteristics are accumulated in a massive set of features, which entail either qualitative and quantitative image descriptors measured within the segmented volume of interest or features “engineered” through advanced AI techniques and deep learning. Such features might be considered as byproducts and manifestations of the genomic variation at the cellular level, which controls the specific disease phenotype and/or response to treatment. Alternatively, the genotypic markers from molecular biology reflect many aspects of gene and protein interactions across a variety of cellular processes relevant to cancer diagnosis and prognosis. Considering the imaging/radiomic features as “surrogates” of the genetic substrate, this talk addresses the intersection of the traditional analysis of differential genes and the feature extraction from medical images. More specifically, we consider the interaction among imaging features (e.g. size and/or shape features, image intensity histogram metrics, texture features) and RNA transcripts measurements (expression of selected genes), which can be used to enhance the predictive power in the diagnosis of cancer. Following this consideration, we explore the modeling of radio-transcriptomics correlations based on the modeling of associations between these two multiscale modalities, i.e. imaging and genomics.

IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy.