Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2019/2020

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione Estiva. Jul 16, 2020 Jul 16, 2020
Sessione Autunnale. Oct 15, 2020 Oct 15, 2020
Sessione Invernale. Mar 18, 2021 Mar 18, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D F G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Combi Carlo

carlo.combi@univr.it 045 802 7985

Constantin Gabriela

gabriela.constantin@univr.it 045-8027102

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Franco Giuditta

giuditta.franco@univr.it +39 045 802 7045

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Laudanna Carlo

carlo.laudanna@univr.it 045-8027689

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Malerba Giovanni

giovanni.malerba@univr.it 045/8027685

Marcon Alessandro

alessandro.marcon@univr.it +39 045 802 7668

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Perduca Massimiliano

massimiliano.perduca@univr.it +39 045 802 7984

Sala Pietro

pietro.sala@univr.it 0458027850

Salvagno Gian Luca

gianluca.salvagno@univr.it 045 8124308-0456449264

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

ModulesCreditsTAFSSD
Final exam
24
E
-

2° Year

ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
English B2
4
F
-
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
2
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S004554

Credits

6

Coordinatore

Alessandro Daducci

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Language

English

The teaching is organized as follows:

Teoria

Credits

4

Period

I semestre

Academic staff

Alessandro Daducci

Laboratorio

Credits

2

Period

I semestre

Academic staff

Alessandro Daducci

Learning outcomes

The course aims at providing students with the applied and theoretical basis for processing biomedical images and extract useful information from them to support the diagnosis process.

At the end of the course, the student shall demonstrate that he/she can apply the material discussed in the lectures to solve effectively the most common issues that may happen throughout a typical analysis pipeline, from the acquisition of the raw images to the correct interpretation of the information extracted from them.

In particular, at the end of the course the student shall demonstrate to be able to:
-- understand the basic physics principles behind image acquisition and formation with the major imaging modalities (X-rays, CT, MRI, PET, US), as well as advantages, disadvantages and peculiarities of each modality;
-- open, manipulate and correctly interpret the multidimensional data acquired with such modalities, which represent specific physical and biological features of the tissue/organ under exam;
-- develop an analysis pipeline to extract useful information from such biomedical images and help the diagnostic process, applying at each step the most adequate processing choices for the specific data at hand.

At the end of the course, the student shall demonstrate the ability to effectively interact with different collaborators having specific backgrounds typically required in a clinical study based on medical imaging, e.g. engineers, physicists, physicians etc.

He/she will also have the required foundations to be able to elaborate further on any scientific, methodological and recent advances in the field beyond the content of the lectures to extend such basic techniques to diverse and more complex analysis scenarios.

Program

(1) Basic concepts
- Image properties: pixel vs voxel, spatial resolution, orientation, data type etc
- File formats: DICOM, NIFTI, MINC etc
- Signal-to-noise (SNR) vs Contrast-to-noise (CNR) ratio
- Noise, blurring and modality-specific artifacts

(2) Overview of major medical imaging modalities
- Radiography: X-rays projection, fluoroscopy and computed tomography (CT)
- Nuclear medicine: SPECT and PET
- Ultrasounds (US)
- Magnetic Resonance Imaging (MRI)

(3) Medical image registration
- Geometric transformations
- Features and similarity measures
- Transformations (linear vs non-linear)

(4) Morphometry analysis
- Region-of-interest analysis
- Voxel-based morphometry
- Surface-based morphometry
- Tract-based morphometry in white matter

(5) Structural connectivity estimation
- Diffusion MRI: principles and main applications
- Local reconstruction: DTI, DSI, CSD etc
- Tissue microstructure estimation: axon diameter mapping, AxCaliber, ActiveAx, CHARMED, NODDI etc
- Tractography: local vs global methods, probabilistic, recent advances

(6) Functional connectivity estimation
- Physiology of neurons and how to record their activity
- Functional MRI: principles and main applications
- Elettroencefalography (EEG) and Magnetoencefalography (MEG): principles and main applications
- Static vs dynamic connectivity

(7) Connectivity analysis (connectomics)
- A network representation of the brain: how and why?
- Studying brain networks with graph theory: concepts and measures
- Comparing brain networks in different groups of subjects

(8) Laboratory
- Hands-on activities on the topics covered throughout the course
- Real neuroimaging data provided to analyze

Examination Methods

The grade will be based on a discussion about the final project assigned during the course. The final project is a very important part of the course, as it allows students to synthesize the concepts learned throughout the course, understand the motivation behind each modality, experiment typical problems that arise in daily-life medical images and apply the appropriate techniques to improve image quality and extract useful information.

Type D and Type F activities

1° periodo di lezioni From 9/30/19 To 12/14/19
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Not yet assigned
I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
1° 2° Python programming language D Maurizio Boscaini (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
1° 2° CyberPhysical Laboratory D Andrea Calanca (Coordinatore)
1° 2° C++ Programming Language D Federico Busato (Coordinatore)
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Corso Europrogettazione D Not yet assigned
1° 2° The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Matteo Cristani

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Gestione carriere


Graduation


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.