Early-stage researchers

TRABIT is training the following 15 PhD students (click on the pictures to get detailed information):

WP1: Multiple Sclerosis WP3: Brain Tumors
Maria Ines Meyer Francesco La Rosa Stefano Cerri Ivan Ezhov Sveinn Pálsson Andrey Zhylka Daniel Krahulec Luca Canalini
WP2: Fetal Brain Disorders WP4: Stroke/Neurovascular Disease
 
Lucas Fidon Thomas Yu Athena Taymourtah Suprosanna Shit Carmen Moreno Genis Amnah Mahroo Ezequiel de la Rosa  
 

Maria Ines Meyer (ESR-1 - Icometrix - Belgium)

  • Bio: My name is Ines and I come from Sines, a small city in the Portuguese coast. I have a masters degree in Biomedical Engineering and Biophysics from the University of Lisbon. During my master's thesis I worked with EEG signal analysis, which I continued to do for some months at a Neurosystems Laboratory of the University of Chile. I have some experience as a researcher in the medical imaging field, which I gained at the Biomedical Imaging Lab at INESC TEC Porto, one of the most prominent research institutions in Portugal. There, most of my research was related to the development of computational systems that could perform automatic retinal image analysis, with a focus on deep neural networks.
  • Project: The progression of Multiple Sclerosis (MS) is related to brain atrophy and the development of brain lesions. In order to track the evolution of the disease patients are generally followed-up over the course of years, and brain volume is calculated from Magnetic Resonance Images (MRI), which can be performed in more than one scanner or center over time. This can represent a problem, because it is known that different scanners produce images with different characteristics which influence the volumetric measurements performed on them. Even scanner updates or changes in protocol can result in different volume measurements, which decrease the reliability of the atrophy evaluation.
    My project focuses on this problem and aims to investigate ways to improve this issue both as a post-processing harmonization step, and at the image level.
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Francesco La Rosa (ESR-2 - EPFL - Switzerland)

  • Bio: Lorem
  • Project: Id .
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Stefano Cerri (ESR-3 - DTU - Denmark)

  • Bio: I'm Stefano Cerri. I'm from Inzago, a small town close to Milan, in Italy. I have a bachelor degree in Computer Science and a master degree in Artificial Intelligence and Robotics, both from Politecnico di Milano. My master thesis was done in collaboration with a AI startup called Horus Technology and it was focused in develop deep learning descriptors to recognize objects from a small device for blind people.
  • Project: Multiple Sclerosis (MS) is the most common inflammatory disorder of the central nervous system and it's characterized by the formation of lesions and a general loss of brain tissue (atrophy). Due to its ability to visualize lesion formation, Magnetic Resonance (MR) imaging has emerged as the central tool to diagnose and monitor MS disease. Automated analysis of MR images of MS patients is complicated by the lack of specificity and sensitivity of the signal in conventional MR sequences, the lack of a standardized clinical protocol to acquire MR images across centers and the need to track tiny changes in longitudinally acquired data.
    My project is based on develop novel computational models to both detect white matter lesions and dozens of neuroanatomical structures in clinical MR scans of MS patients, and to optimally combine those measurements into sensitive biomarkers of brain disease.
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Lucas Fidon (ESR-4 - KCL - UK)

  • Bio:
    My background is Mathematics and Computer Science.
    I am graduated from CentraleSupélec, one of France’s leading engineering schools.
    In parallel, I also received Master degrees from ENS Paris-saclay (MVA) and Paris-Sud University.
  • Project: 
    I am developing new computational tools for brain fetal Magnetic Resonance Images (MRI) analysis to improve treatment for fetus suffering from Spina Bifida.
    ◉ Advisor: Prof. Tom Vercauteren.
    ◉ Research Interests: MRI reconstruction and super resolution, robust and accurate biomarkers measure and longitudinal data analysis.
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Thomas Yu (ESR-5 - EPFL - Switzerland)

  • Bio: Lorem
  • Project: Id .
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Athena Taymourtah (ESR-6 - MUW - Austria)

  • Bio: Lorem
  • Project: Id .
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Ivan Ezhov (ESR-7 - TUM - Germany)

  • Bio: Lorem
  • Project: Id .
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Sveinn Pálsson (ESR-8 - DTU - Denmark)

  • Bio: I have a bachelor degree in electrical and computer engineering from the University of Iceland. I worked for one year in signal processing and machine learning research for sleep diagnostics at Nox Medical.
    I did my master degree in information technology and electrical engineering at ETH in Zurich. My master thesis was about separation of audio signals with deep learning models.
  • Project: The goal of my PhD project is to develop accurate and interpretable prediction models for brain tumor grading and tumor recurrence detection from joint PET/MRI scans, taking advantage of the increase in sensitivity and specificity of dynamic FET PET data compared to conventional MRI.
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Andrey Zhylka (ESR-9 - TU/e - The Netherlands)

  • Bio: My name is Andrey. Originally I come from Minsk, the capital of Belarus, where I obtained Specialist and Master degrees in Mathematics and Computer Science from Faculty of Applied Mathematics and Computer Science of Belarusian State University (BSU). My graduation project was about cerebrovascular segmentation and analysis of 3D Magnetic Resonance Angiography images. And my master thesis was focused on breast X-ray image analysis using deep learning. In spring 2015 I was a DAAD intern at University Hospital of Jena where I worked on vascular OCT video analysis. I also have some teaching and industrial experience, which overall includes such spheres as discrete mathematics, software engineering, stereo image reconstruction, computer vision, machine learning and deep learning.
  • Project: Given that our brain contains an enormous amount nerve fibres, often grouped in bundles, which are essential for us to be in command of our senses and our body, it is of high importance that in case of brain tumour surgery they would not be harmed. Thus neurosurgeon should be well informed of where and how close to the tumour nerve fibres are located. This information can be gained from Diffusion Weighted Magnetic Resonance Image.
    Thus the main focus of my project is to develop robust and accurate algorithms for fibre tracking in brains of the patients with tumours based Diffusion Weighted Images.  Another goal is to provide uncertainty and risk estimation in order to inform neurosurgeons about fibres being dangerously close to tumour or about the potential errors in the results of fibre tracking.
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Daniel Krahulec (ESR-10 – Philips – The Netherlands)

  • Bio: My name is Daniel Krahulec and I come from the marvelous mountain-rimmed lands of the Czech Republic. I grew up as a villager in a three-generation detached house. Embarking on university studies, I first pursued my Bachelor's degree in the Electrical Engineering program, consisting of numerous courses in biomedical engineering at the Technical University of Ostrava, and medicine in the Faculty of Medicine at University of Ostrava. In 2014, I spent a five-month exchange period at Tampere University of Technology (Tampereen Teknillinen Yliopisto, Finland), where I peeked into the Master's program of Biomedical Engineering and filled my study plan with courses in radiology and radiotherapy. One year later, I gained admission to the freshly launched study program of Human Neuroscience and Neurotechnology at Aalto University (Aalto-yliopisto, Finland), where I graduated with honours in December 2017.

  • Project: In the TRABIT network, I have been working on a PhD project focusing on industrial design. As opposed to the usual scientific PhD with the aim of publishing novel research articles, my primary goal is to develop a prototype of a clinical software application that will be applicable in tumor neurosurgery planning, intrasurgical guidance, as well as postoperative neurooncological follow-up. Firstly, a thorough analysis of clinical needs is conducted, during which customers (neurosurgeons and neuroradiologists) are interviewed about clinical workflow in different hospitals. Next, these clinical needs must be transformed into functional blocks of the prototype application running in a Philips internal software development environment. Further steps include designing a user interface and software architecture, as well as testing, in-house verification, technical validation, and clinical evaluation of the final solution.

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Luca Canalini (ESR-11 - MEVIS - Germany)

  • Bio: My name is Luca and I come from Pietrasanta, a town on the Tuscanian coast of Italy. I obtained my bachelor in Biomedical Engineering at the University of Pisa. There, in collaboration with the Center for Computer Assisted Surgery (EndoCAS), I conducted a thesis related to an augmented reality application for the synthesis of long bones fractures. I continued then my education with a master degree in Medical Imaging and Applications (MAIA), an Erasmus Mundus Joint Master organized by three different universities in Europe. During my master thesis at the ViCOROB research group (University of Girona), I focused on the application of deep neural networks to localize, segment and characterize skin lesions on full body images.
  • Project: In brain surgery, the survival of glioma patients correlates with the extent of resection. However, the intra-operative delineation of pathological tissue and functional brain imaging has not yet been resolved or requires considerable technical and time expenditure during the operating procedures. Moreover, the neuro-navigation based on pre-operative image data is limited by an increasing brain shift during the resection. In this context, intra-operative ultrasound can update the anatomical information, but its use is clinically insufficient if not combined with a high-quality pre-operative imaging technique, such as MRI. The goal of my PhD thesis is to use segmentation and registration techniques to combine intra-operative ultrasound with preoperative MRI to assist brain surgery in glioma removal.
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Suprosanna Shit (ESR-12 - TUM - Germany)

  • Bio: Lorem
  • Project: Id .
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Maria del Carmen Moreno Genis (ESR-13 - RegionH - Denmark)

  • Bio: I am Maria del Carmen Moreno Genis, a Mexican girl coming from the beautiful state of Oaxaca. In Mexico, I studied my bachelor degree in Biomedical Engineering at ‘Universidad Politecnica de Chiapas’. During my studies I formed part of a research project, focus on Thermal Imaging for breast cancer detection, in conjunction with the research centre ‘Centro de Estudios y Prevención del Cancer A.C’ (CEPREC).  After graduating, I starting working at CEPREC as a research assistant and clinical trial coordinator at the same time. Later on, I formed part of an Eramus Mundus Joint Master program in Medical Imaging and Applications (MAIA), taking place at 3 universities from France, Italy and Spain. I obtain my Master degree with my thesis “False positive reduction for lesion detection in breast mammography based on two-views lesion correspondence strategy “, which I did at the VICOROB research group from the University of Girona.
  • Project: Id .
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Amnah Mahroo (ESR-14 - MEVIS - Germany)

  • Bio: Lorem
  • Project: Id .
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Ezequiel de la Rosa (ESR-15 - Icometrix - Belgium)

  • Bio: Lorem
  • Project: Id .
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