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.

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 sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esami Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione Estiva Lauree Magistrali Jul 19, 2018 Jul 19, 2018
Sessione Autunnale Lauree Magistrali Oct 18, 2018 Oct 18, 2018
Sessione Invernale Lauree Magistrali Mar 21, 2019 Mar 21, 2019
Holidays
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
Vacanze estive Aug 6, 2018 Aug 19, 2018

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 G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Belussi Alberto

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

Bicego Manuele

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

Bombieri Cristina

cristina.bombieri@univr.it 045-8027284

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

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

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.

Modules Credits TAF SSD
Between the years: 1°- 2°
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

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

The teaching is organized as follows:

Teoria

Credits

4

Period

II semestre

Academic staff

Alessandro Daducci

Laboratorio

Credits

2

Period

II 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
- Signal representation: frequency domain, spherical harmonics, sparse bases

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

(3) Basic image processing
- Recall of elementary tools: filtering, edge detection and image enhancement
- Registration: features, similarity measures, transformations (linear vs non-linear)

(4) Connectivity analysis with 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

(5) Laboratory
- Introduction to Python
- Hands-on activities on the topics covered throughout the course

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

Modules not yet included

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.

Graduation


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.

Career management


Area riservata studenti