Speaker: Chao Chen, City University of New York (CUNY)
Thanks to advanced imaging techniques, we are able to study complex and dynamic biomedical systems through high-resolution images. These images bring new opportunities as well as challenges; existing global priors such as shape, smoothness, and sparsity may not be sufficient. In this talk, we explore new global structural information underlying these data. In cardiac image analysis, we explicitly model topological handles in order to extract and analyze complex interior surfaces of human ventricles. For electron microscopic (EM) images, we construct graphical models for neuron cell extraction. Furthermore, to better solve the ambiguity and assist human proofreading, we compute multiple highly probable solutions rather than one from these graphical models.
Chao Chen is an Assistant Professor at City University of New York (CUNY). His interdisciplinary research lies in between biomedical image analysis, machine learning, and computational topology. His research has been published in top venues in all these domains. For more information, please visit his website.