Vés enrere PhD Student Jordina Torrents Barrena is awarded for her Doctoral Thesis at the 5th Edition of the Big Data & AI Talent Awards 2019

PhD Student Jordina Torrents Barrena is awarded for her Doctoral Thesis at the 5th Edition of the Big Data & AI Talent Awards 2019

The "Big Data & AI Talent Awards 2019" are given in the framework of the Big Data & AI Congress, a reference event in the field of big data analytics and artificial intelligence. It is organized by Big Data CoE and takes place every year in Barcelona.




On the 5th edition of this event, the UPF PhD student Jordina Torrents –who will officially defend her PhD thesis on 9th December 2019– has been awarded with a 1.500 € prize for her Doctoral Thesis“Deep learning-based segmentation methods for computer-assisted fetal surgery: Application to Twin-to-Twin Transfusion Syndrome”.

Jordina has been working on her doctoral thesis for three years at Simulation, Imaging and Modelling for Biomedical Systems (SiMBioSys) research group lead by Miguel Ángel Gonzalez Ballester (ICREA research professor) and linked to the UPF ICT Department.
These prizes, sponsored by Oracle, aim to give recognition to the doctoral thesis and master or postgraduate final projects related to big data analytics and artificial Intelligence with highest socioeconomic impact and technological innovation.

Cutting edge technology in foetal surgery

Jordina Torrents’s research focuses on the complex task of building specific 3D models and computational tools for foetal surgery planning and navigation. She is working in the areas of image processing, computer vision, artificial intelligence, machine / deep learning and the development of software systems for surgical planning and guidance.

Her thesis specifically focuses on the development of deep learning-based algorithms for the detection and segmentation of fetal structures in magnetic resonance imaging (MRI) and 3D ultrasound (US) images. Special attention is laid on monochorionic twins affected by the twin-to-twin transfusion syndrome (TTTS). The thesis proposes the first TTTS fetal surgery planning and simulation platform, which incorporates novel techniques into a flexible C++ and MITK based application.

This software allows full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, helping to determine the best entry point for the fetoscope into the mother’s womb, and training surgeons’ movements and trajectories ahead of operation.


From the ICT Department, we would like to congratulate her for her outstanding work!



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