Professor Massoud Zolgharni

Professor of Computer Vision

School of Computing and Engineering

massoud.zolgharni@uwl.ac.uk

Massoud Zolgharni is a Professor of Computer Vision at the School of Computing and Engineering, having joined the School as a senior lecturer in August 2018, and later as Associate Professor in January 2020. Previously, he was a senior lecturer at the University of Lincoln (2015-2018), and Research Associate at the National Heart and Lung Institute, Imperial College London (2010-2015). He is a Fellow of the Higher Education Academy. Massoud received his BSc degree in Mechanical Engineering from Amirkabir University of Technology, and completed his Master’s and PhD studies at Brunel and Swansea Universities, UK.

His research interests lie in the area of computer vision, in general, and medical imaging, in particular. In recent years, he has focused on developing automated techniques for low-cost and non-invasive cardiac imaging applications. Massoud has been lecturing at the undergraduate and postgraduate levels, covering a wide range of courses including Research Methods, Image Processing, and Computer Architectures, Parallel Computing, Artificial Intelligence, Machine Learning, and Computer Vision.

Massoud is the Course Leader for MSc Artificial Intelligence and PhD Courses within the School of Computing and Engineering. He is a member of the University Research Impact Group and the University Research Degree Sub-Committee, and serves as the Critical Reader for the School. He is also an ex-officio member of the School Board. Massoud organises and chairs the School’s monthly research seminar.

He is also the founder and head of the Intelligent Sensing and Vision research Lab which has fostered interdisciplinary research by providing a framework for cross-faculty collaborative working. The Lab is financially supported by the British Heart Foundation, and collaborates with several industrial partners, NHS Trusts and Hospitals across the UK.

Massoud serves as peer reviewer for more than 15 technical and clinical journals, and reviews grant proposals for EPSRC, NIHR, Newton Fund and Researcher Links (British Council). He is also an NVIDIA DLI (Deep Learning Institute) Certified Instructor and University Ambassador.

Qualifications

BSc, MPhil, PhD, FHEA.
Memberships

Fellow
Higher Education Academy (HEA)
University Ambassador and Certified Instructor
Deep Learning Institute (NVIDIA DLI)
Member
European Society of Cardiology (ESC)

Research

See Professor Massoud Zolgharni's publications list in the UWL Repository.

Current grants:

2022-2027: British Heart Foundation Programme Grant (RG/F/22/110059), UNITY: UK Collaborative for integrating AI into echocardiography. PI at UWL £305k (Total £1.5m)
2020-2023: British Heart Foundation Project Grant (PG/19/78/34733), Tensor-mapping to resolve the Achilles heel of echocardiographic strain imaging. PI at UWL £150k (Total £257k)

Past grants:

2011-2017: European Research Council Starting Grant (ERC-2011-StG_20101109), Rapid reliable volume status and ventricular function assessment by non-specialist staff using innovative imaging in clinical practice. Co-I, €1.54m

Research and publications

Patents
2013 ∙ ECHOCARDIOGRAPHY ∙ WO/2014/076498
2005 ∙ MICROFLUIDIC DEVICE ∙ WO/2008/084245
Journal articles
N. Ghatwary, M. Zolgharni, F. Janan, X. Ye. Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos, IEEE Journal of Biomedical and Health Informatics. 2020; DOI: 10.1109/JBHI.2020.2995193.
J. Howard; M. Shun-Shin; M. Zolgharni; D. Francis, Improving ultrasound video classification: a comparison of state-of-the-art deep learning methods in echocardiography, Nature Digital Medicine. 2019; submitted.
N. Azarmehr; X. Ye; M. Zolgharni, An Optimisatiation-Based Iterative Approach for Speckle Tracking Echocardiography, Medical & Biological Engineering & Computing. 2020; 58:1309–1323.
N. Ghatwary, X. Ye, M. Zolgharni, Esophageal abnormality detection using DenseNet based Faster R-CNN with Gabor features. IEEE Access. 2019: 84374-84385.
N. Ghatwary, M. Zolgharni, X. Ye. Early esophageal adenocarcinoma detection using deep learning methods, International Journal of Computer Assisted Radiology and Surgery. 2019; 0, 1-11.
S. Sacchi, N.M. Dhutia, M.J. Shun-Shin, M. Zolgharni, N. Sutaria, D.P. Francis, G.D. Cole. Doppler assessment of aortic stenosis: reading the peak velocity is superior to velocity time integral, European Heart Journal - Cardiovascular Imaging. 2018; 0, 1-10.
M. Zolgharni; M. Negoita; N.M. Dhutia; S.A. Sohaib; J.A. Finegold; S. Sacchi; G.D. Cole; D.P. Francis. Automatic Detection of End-Diastolic and End-Systolic Frames in 2D Echocardiography. Echocardiography. 2017; 1-12.
Dhutia NM, Zolgharni M, Mielewczik M, Negoita M, Sacchi S, Manoharan K, Francis DP, Cole GD. Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis. The international journal of cardiovascular imaging. 2017 Feb 20.
Negoita M, Zolgharni M, Dadkho E, Pernigo M, Mielewczik M, Cole GD, Dhutia NM, Francis DP. Frame rate required for speckle tracking echocardiography: A quantitative clinical study with open-source, vendor-independent software. International journal of cardiology. 2016 Sep 1;218:31-6.
Cole GD, Dhutia NM, Shun-Shin MJ, Willson K, Harrison J, Raphael CE, Zolgharni M, Mayet J, Francis DP. Defining the real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation. The international journal of cardiovascular imaging. 2015 Oct 1;31(7):1303-14.
Dhutia NM, Cole GD, Zolgharni M, Manisty CH, Willson K, Parker KH, Hughes AD, Francis DP. Automated speckle tracking algorithm to aid on-axis imaging in echocardiography. Journal of Medical Imaging. 2014 Oct 1;1(3):037001-.
Dhutia NM, Zolgharni M, Willson K, Cole G, Nowbar AN, Dawson D, Zielke S, Whelan C, Newton J, Mayet J, Manisty CH. Guidance for accurate and consistent tissue Doppler velocity measurement: comparison of echocardiographic methods using a simple vendor-independent method for local validation. Eur Heart J Cardiovasc Imaging. 2014 Jul 1;15(7):817-27.
Zolgharni M, Dhutia NM, Cole GD, Bahmanyar MR, Jones S, Sohaib SA, Tai SB, Willson K, Finegold JA, Francis DP. Automated aortic Doppler flow tracing for reproducible research and clinical measurements. IEEE transactions on medical imaging. 2014 May;33(5):1071-82.
Zolgharni M, Griffiths H, Ledger PD. Frequency-difference MIT imaging of cerebral haemorrhage with a hemispherical coil array: numerical modelling. Physiological measurement. 2010 Jul 21;31(8):S111.
Zolgharni M, Ledger PD, Griffiths H. Forward modelling of magnetic induction tomography: a sensitivity study for detecting haemorrhagic cerebral stroke. Medical & Biological Engineering & Computing. 2009 Dec 1;47(12):1301.
Zolgharni M, Ledger PD, Armitage DW, Holder DS, Griffiths H. Imaging cerebral haemorrhage with magnetic induction tomography: numerical modelling. Physiological measurement. 2009 Jun 2;30(6):S187.
Zolgharni M, Jones BJ, Bulpett R, Anson AW, Franks J. Energy efficiency improvements in dry drilling with optimised diamond-like carbon coatings. Diamond and Related Materials. 2008 Oct 31;17(7):1733-7.
Azimi SM, Bahmanyar MR, Zolgharni M, Balachandran W. Numerical investigation of magnetic sensor for DNA hybridization detection using planar transformer. The International Journal of Multiphysics. 2007 Dec 31;1(4).
Zolgharni M, Azimi SM, Bahmanyar MR, Balachandran W. A numerical design study of chaotic mixing of magnetic particles in a microfluidic bio-separator. Microfluidics and Nanofluidics. 2007 Dec 1;3(6):677-87.
Conferences

Refereed conference proceedings
A. Amer, X. Ye, M. Zolgharni, F. Janan, GFD Faster R-CNN: ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montréal, Canada, July 2020
N. Ghatwary; M. Zolgharni; X. Ye, GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for Automatic Detection of Esophageal Abnormalities in Endoscopic Images, 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019), Shenzhen, China, October 2019
N. Azarmehr; X. Ye; F. Janan; M. Zolgharni, Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning, Medical Imaging with Deep Learning (MIDL), London, July 2019
N. Azarmehr; X. Ye; M. Zolgharni, Segmentation of Left Ventricle in 2D echocardiography using deep learning, Medical Image Understanding and Analysis (MIUA), Liverpool, July 2019
N. Ghatwary; X. Ye; M. Zolgharni, Evaluation of early esophageal adenocarcinoma detection using deep learning, Computer Assisted Radiology and Surgery (CARS), Berlin, June 2018
G.D. Cole, N.M. Dhutia, M. Shun-Shin, K. Willson, M. Zolgharni, J. Mayet, D.P. Francis. Reproducibility of visual grading of left ventricular function and estimation of ejection fraction: dependence on image quality, European Heart Journal - Cardiovascular Imaging, Vol: 15 Suppl 2, Pages: ii65-ii67, ISSN: 2047-2404
M. Zolgharni, N.M. Dhutia, G.D. Cole, K. Willson, D.P. Francis. Feasibility of using a reliable automated Doppler flow velocity measurements for research and clinical practices, 2014 SPIE Medical Imaging, San Diego, California, February 15-20
N.M. Dhutia, M. Zolgharni, G.D. Cole, K. Willson, D.P. Francis. Calibration of echocardiographic tissue Doppler velocity, using simple, universally-applicable methods, 2014 SPIE Medical Imaging, San Diego, California, February 15-20
H. Griffiths, M. Zolgharni, P.D. Ledger, S. Watson. The Cardiff Mk2b MIT head array: optimising the coil configuration, 2010 EIT Conference, Gainesville, Florida, April 4-8
M. Zolgharni, P.D. Ledger, H. Griffiths. Difference imaging of cerebral stroke in the human brain using edge finite element simulation of MIT, 1st International Conference on Mathematical and Computational Biomedical Engineering, CMBE 2009, pp.288-91, Swansea, UK
S. Watson, H.C. Wee, R. Patz, R.J. Williams, M. Zolgharni, H. Griffiths. Detection of peripheral haemorrhagic cerebral stroke by magnetic induction tomography: phantom measurements, 2009 EIT Conference, Manchester, UK, June 16-19
M. Zolgharni, P.D. Ledger, H. Griffiths. High-contrast frequency-difference imaging for magnetic induction tomography, 2009 EIT Conference, Manchester, UK, June 16-19
M. Zolgharni, P.D. Ledger, H. Griffiths. Imaging cerebral haemorrhage with MIT: frequency-difference imaging with a customised coil array, 2009 EIT Conference, Manchester, UK, June 16-19.
B. Dekdouk, M.H. Pham, D.W. Armitage, C. Ktistis, M. Zolgharni, A.J. Peyton. A feasibility study on the delectability of edema using magnetic induction tomography using an analytical model, 2008 IFMBE Proc. 22:736-9 ISBN 978-3-540-89207-6
M. Zolgharni, H. Griffiths, D.S. Holder. Imaging haemorrhagic cerebral stroke by frequency-difference magnetic induction tomography: numerical modelling, 2008 IFMBE Proc. 22 2464-7
Y. Maimaitijiang, S. Watson, M.A. Roula, M. Zolgharni, H. Griffiths, R.J. Williams. An iterative absolute image reconstruction algorithm for magnetic induction tomography, 2008 EIT Conference, Dartmouth NH, USA, June 16-18.
M. Zolgharni, P.D. Ledger, D.W. Armitage, H. Griffiths, D.S. Holder. Detection of haemorrhagic cerebral stroke by magnetic induction tomography: FE and TLM numerical modelling, 2008 EIT Conference, Dartmouth NH, USA, June 16-18.
M. Zolgharni, P. D. Ledger, H. Griffiths, D. S. Holder. Numerical modelling of the cerebral stroke in human brain, 16th UK Conf. on Computational Mechanics (ACME-UK), Newcastle upon Tyne 2008, pp.139-142, ISBN 978-0-7017-0218-2
S.M. Azimi, M.R. Bahmanyar, M. Zolgharni, W. Balachandran .Using spiral inductors for detecting hybridization of DNAs labeled with magnetic beads, 10th Annual NSTI Nanotechnology Conf., Santa Clara, California, May 20-24, 2007, pp 567-570
M. Zolgharni, S.M. Azimi, M.R. Bahmanyar, W. Balachandran .A microfluidic mixer for chaotic mixing of magnetic particles, 10th Annual NSTI Nanotechnology Conf., Santa Clara, California, May 20-24, 2007, pp 336-339
S.M. Azimi, M.R. Bahmanyar, M. Zolgharni, W. Balachandran. An inductance-based sensor for DNA hybridization detection, Proc. 2nd IEEE Int. Conf. on Nano/Micro Engineered and Molecular Systems, January 16-19, 2007, Bangkok, Thailand, 524-57
M. Zolgharni, S.M. Azimi, H. Ayers, W. Balachandran. Labelling of biological cells with magnetic particles in a chaotic microfluidic mixer, Proc. 2nd IEEE Int. Conf. on Nano/Micro Engineered and Molecular Systems, January 16-19, 2007, Bangkok, Thailand, 55-8
Conference abstracts
M. Zolgharni; N.M. Dhutia; M. Negoita; G.D. Cole; D.P. Francis. Multi-modality echocardiographic assessment of left ventricular function, 2017 IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, April 18-21
M. Zolgharni, M. Negoita, E. Dadkho, G.D. Cole, S.M.A. Sohaib, N.M. Dhutia, D.P. Francis. Automatic detection of end-systole and end-diastole frames, 2015 IEEE International Symposium on Biomedical Imaging, NY, USA, April 16-19
Research degree supervision

Principal Supervisor
A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. (Henrique De Melo Ribeiro)
Deep Learning Approach for Automatic Phase Detection in Electrocardiograms. (Elisabeth Lane)
Explainable Artificial Intelligence in Echocardiography. (Jevgeni Jevsikov)
Efficient Approaches for Creating a Biobank of Expert Annotations for Deep Learning Developments in Echocardiography. (Eman Alajrami)
Second Supervisor
Leveraging AI-based technology for data sourcing and improvement of SUDS performance. (Cristiane de Fatima Donde Girotto)
Respiratory systems disorders investigation using sound, bioimpedance measurements, and artificial intelligence. (Julia Zofia Tomaszewska)
Development of a novel Electroglottography sensors system, using advanced electronics design and deep learning, with improved applicability and usability. (Eugenio Donati)
Identifying cardiovascular irregularities using IoT and big data analytics pertaining to seizure detection. (Jamie Pordoy)
Neural Architecture Search for the Development of Real-Time Virtual Analogue Audio Effects. (Peter Dowsett)
Automatic Pre-eclampsia recognition - an application of artificial intelligence to detect women at risk of developing Pre-eclampsia in early pregnancy. (Kumar Puvanendran)
Past PhD Students
Automatic Classification and Quality Assessment of Cardiac Echo Images using Deep Convolutional Neural Network. (Robert Labs, Principal Supervisor, 2022)
Developing a fully automated, vendor-independent, and reliable framework to assess the left ventricle function. (Neda Azarmehr, Principal Supervisor, 2021)
Early esophageal adenocarcinoma detection using deep learning methods. (Noha Ghatwary, Second Supervisor, 2020)