Ariel Hernán Curiale graduated from the University of Buenos Aires, Argentina, where he obtained his B.Sc in computer science, in 2009. In 2011 he received the M.S. degree in Information and Telecommunication Technology from University of Valladolid, Valladolid, Spain. He joined the the Laboratory of Image Processing, University of Valladolid, as a Researcher in 2010 with a Collaboration Fellowship granted by the Argentine Ministry of Education and the Caroline Foundation. He also received a scholarship from the University of Valladolid and has been a student visitor at the Erasmus Medical Center, Rotterdam for three months starting in September 2013. In 2015 he received the International Ph.D. degree on information technologies and telecommunications of the Official College of Telecommunications Engineering, University of Valladolid, Valladolid, Spain. Nowadays, he is a researcher (CONICET) working the Medical Physics department at the Centro Atómico Bariloche – Instituto Balseiro.
“His research interest are focused on medical applications of image analysis. This includes machine learning and local image structure using tensor analysis, image segmentation, and image registration.”
The primary focus of his research has been in the area of signal processing and computer science on the field of medical image analysis, especially developing new algorithms and their application in clinical applications. Major applications of the research have included the areas of image segmentation, image registration and detection of thin-layered structures such as valvular structures.
“His thesis dissertation focused on two main tasks: (1) the characterization of the dynamic properties of the left ventricle and (2) the detection of different structures therein.”
For this purpose, methods for motion and strain estimation were proposed, as well as a method for identifying different cardiac structures, such as the mitral valve, the long axis of the left ventricle and the aortic valve. Currently, his work is focused on the field of neural network, especially on Deep Learning techniques for image classification and quantification.