Abhirup Banerjee

PhD, MStat, BSc (Hons)

Postdoctoral Researcher

As a postdoctoral researcher, working with Prof. Robin Choudhury (CVM) and Prof. Vicente Grau (IBME), my research work is mainly focused on the Development of an automated system to co-register 3D coronary arterial (CA) tree from 2D x-ray angiograms with corresponding 3D volume rendering of the heart from cardiac magnetic resonance (CMR).

I have developed a novel solution for automated 3D CA tree reconstruction from multiple 2D angiographic projections and currently working on the application of 3D model for non-invasive extraction of clinically significant information. Along with my main research in cardiovascular imaging, I am involved in few biomedical imaging projects, such as 3D reconstruction of cerebro-vascular tree, coronary sinus, etc. I am also a Stipendiary Lecturer in Engineering Science in the Mansfield College, Oxford.

I have received the BSc (Hons) and Master degree both in Statistics from the University of Calcutta and the Indian Statistical Institute, respectively. I have obtained my Ph.D. degree in Computer Science from the Indian Statistical Institute, Kolkata, for my work on Automated Segmentation of Brain Tissue Classes from MR Images.

I serve as an Innovation Champion in the Oxford University Innovation and a Mental Health First Aider in the RDM. In my personal life, I am an avid movie buff and a street photographer.

Key publications

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections
Journal article

Banerjee A. et al, (2020), IEEE Transactions on Medical Imaging, 39, 1278 - 1290
Rough sets for bias field correction in MR images using contraharmonic mean and quantitative index.
Journal article

Banerjee A. and Maji P., (2013), IEEE Trans Med Imaging, 32, 2140 - 2151

Recent publications

Lesion Size Index (LSI)–guided catheter ablation for atrial fibrillation: can tissue impedance drop help to identify desirable ablation settings and target indices?
Leo M. et al, (2022)
Rapid neutrophil mobilisation by VCAM-1+ endothelial extracellular vesicles.
Journal article

Akbar N. et al, (2022), Cardiovasc Res
Combined Generation of Electrocardiogram and Cardiac Anatomy Models Using Multi-Modal Variational Autoencoders
Conference paper

Beetz M. et al, (2022), Proceedings - International Symposium on Biomedical Imaging, 2022-March
Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders
Chapter

Beetz M. et al, (2022), Lecture Notes in Computer Science, 75 - 83
Predicting 3D Cardiac Deformations with Point Cloud Autoencoders
Chapter

Beetz M. et al, (2022), Lecture Notes in Computer Science, 219 - 228
Semi-Supervised Coronary Vessels Segmentation from Invasive Coronary Angiography with Connectivity-Preserving Loss Function
Conference paper

He H. et al, (2022), Proceedings - International Symposium on Biomedical Imaging, 2022-March
A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices.
Journal article

Banerjee A. et al, (2021), Philos Trans A Math Phys Eng Sci, 379
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.
Journal article

Mamalakis M. et al, (2021), Comput Med Imaging Graph, 94
INtra-procedural ultraSound Imaging for DEtermination of atrial wall thickness and acute tissue changes after isolation of the pulmonary veins with radiofrequency, cryoballoon or laser balloon energy: the INSIDE PVs study.
Journal article

Leo M. et al, (2021), Int J Cardiovasc Imaging
LUCAS: Development and Validation of a Simple Mortality Risk Calculator that Predicts 60-Day Survival of Adult SARS-CoV-2 Positive Patients at Hospital Admission Using Rapid Routine Tests
Journal article

Ray S. et al, (2021), SSRN Electronic Journal

More publications