Skip to main content
Dr Concetta Piazzese PhD

Dr Concetta Piazzese

PhD

Research Associate

School of Engineering

Email
concettap@cardiff.ac.uk
Telephone
+44 (0)29 2087 0022
Campuses
Room N/1.51, Queen's Buildings - North Building, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Users
Available for postgraduate supervision

I am a Research Associate at the School of Engineering. I received a MEng degree in Biomedical Engineering from Politecnico di Milano. In 2012, I started a joint Phd between Politecnico di Milano and Università della Svizzera italiana focused on development cardiac models by means of precise and realistic patient-specific geometries extracted from medical images. I also collaborated with the Noninvasive Cardiac Imaging Laboratory (University of Chicago) and with Cardiocentro (Lugano).

I have a strong experience in medical image processing and algorithm development.

I also have a strong experience in supervising undergraduate students.

My current research interests include: X-ray imaging, CT Imaging, MR Imaging, radiomics, machine learning, clinical studies, medical physics, Radiotherapy Trials Quality Assurance (RTTQA).

Member, Medical Engineering group.

Member, Cancer Imaging and Data Analytics research group.

Member, Research Committee.

Member, Cardiff University Research Staff Association (CURSA).

Part Member of the Center for Computational Medicine in Cardiology (CCMC).

Education and Qualification

2016

Phd in Biomedical Engineering (joint PhD), Politecnico di Milano (Italy) and Università della Svizzera Italiana (Switzerland).

2012

Master’s Degree in Biomedical Engineering, Politecnico di Milano (Italy).

2009

Bachelor’s Degree in Biomedical Engineering, Politecnico di Milano (Italy).

Career overview

2017 – Present

Research Associate, School of Engineering, Cardiff University, Cardiff, UK.

2016 – 2017

Research Fellow, Centro Cardiologico Monzino, Milan, Italy.

2012

Visiting Researcher, The University of Chicago, Chicago, Illinois, United States

2007

Biomedical engineer (intern), Laboratorio di Ingegneria Clinica, Fondazione San Raffaele del Monte Tabor, Milan

Honours and awards

2016

Best Poster Award at the 12th IEEE-EMBS International Summer School on Biomedical Imaging, Saint-Jacut de la Mer, France, June 16th - June 24th.

2013

Rosanna Degani Young Investigator's Finalist Award, IEEE Computers in Cardiology.

2020

2019

2018

2020 – Present

Co-leader of the module Engineering Computing (EN1094).

2017 – Present

Lecturer of the module Medical Image Processing (EN4505).

2016

Tutor of the module Biomedical Image Processing Laboratory (073588) in Politecnico di Milano (March - June 2016).

Research theme: medical engineering, radiomics, Radiotherapy Trials Quality Assurance (RTTQA).

2017 – PRESENT

TEXRAD: Establishing image derived prognostic and predictive biomarkers of radiotherapy treatments and assessing treatment response using texture analysis.

Radiotherapy is used as part of the treatment approach in approximately 40% of patients cured of their disease, but unfortunately not all patients are cured. There is a much work being done to improve outcomes by increasing the radiotherapy dose or combining (novel) drugs with the radiotherapy, but these may add side effects. We therefore want to be able to improve our prediction of which patients will be cured with standard therapy and which will recur, as these have the most to benefit from intensifying the treatment. There are several approaches being pursued, based on samples of the cancer, blood samples or imaging. This study aims to derive more information from the imaging that is routinely taken as part of patients’ care, using locally developed, highly sophisticated analysis programs.

ARENA: Extension of RTTQA outlining activity into the educational arena.

Delineation of radiotherapy target volume has an essential role in modern treatment planning. However, it is affected by intra-interobserver variations and it has been identified as a weakness in radiotherapy planning. For this reason, accurate target volume delineation (TVD) is necessary to ensure optimal tumour coverage. ARENA, a collaborative project among Cardiff University, Velindre Cancer Centre and Singleton Hospital, aims at facilitating higher quality and standardised TVD approach through development of tumour site-specific TVD instructional modules and corresponding outlining module.

2012 – 2016

An inter-modality statistical shape modelling approach for the 3D segmentation of cardiac structures from magnetic resonance images.

Cardiac magnetic resonance (CMR) imaging is considered the reference modality for quantification of ventricular volume and function. Important clinical parameters are still derived from manual segmentation of the data. Different automated or semi-automated segmentation techniques have been proposed to improve reproducibility and preserve accuracy. To this respect, model-based methods, such as statistical shape models (SSM), have become a powerful tool to segment medical images by deforming and matching a predefined geometric shape to the locations of extracted features of the desired structure to be detected. The goal of this project was to develop and optimize an inter-modality SSM approach and adapt it to segment different cardiac structures (such as the LV endocardium and epicardium, and the right ventricular (RV) endocardium) with minimal user interaction.

Past projects

2019

Co-supervisor of the 3rd year project titled “Assessment of radiomics features extracted from T2-weighted MR images of soft-tissue sarcomas of the extremities”.

Co-supervisor of the 3rd year project titled “Assessment of radiomics features extracted from T1-weighted MR images of soft-tissue sarcomas of the extremities”.

2018

Co-supervisor of the 3rd year project titled “Robustness Analysis of Radiomic Features in CT Scans of Head and Neck Cancer Patients”.

Co-supervisor of the 3rd year project titled “Quantification of Radiomics Features Extracted from CT Images with a Semi-Automatic Technique”.

2016

Co-supervisor of the master’s thesis titled “Active shape modeling for the 3D segmentation of left kidneys in patients with polycystic disease (ADPKD) from CT and MR images”.

Co-supervisor of the master’s thesis titled “Sviluppo di un algoritmo per la detezione dei muscoli papillari da immagini di risonanza magnetica”.

2013

Supporting supervisor of the master’s thesis titled “Fusione di Immagini Ecocardiografiche Transesofagee Tridimensionali dell’Aorta Discendente”.