MACHINE LEARNING BASED MORPHOMETRIC IMAGE ANALYSIS OF THE PULMONARY ARTERIAL TREE PREDICTS PRESENCE OF CTEPH

L. Burrows¹, J. Page², J. Suntharalingam², J. Rodrigues², A. N. Cookson¹

¹University of Bath, United Kingdom; ²Royal United Hospitals Bath NHS Trust, United Kingdom

MRI-QUANTITATIVE SUSCEPTIBILITY MAPPING AND DEEP LEARNING FOR THE RISK STRATIFICATION OF CAROTID PLAQUES

J. Bagnall^1,2, F. Digeronimo^1,2, B. Tornifoglio^1,2, C. Lally^1,2,3

¹Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland; ²Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin, Ireland; ³Advanced Materials and Bioengineering Research Centre (AMBER), Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin, Ireland

COMPARISON OF RECONSTRUCTION KERNEL AND MONOCHROMATIC ENERGY PAIRS USED IN DUAL ENERGY CT IMAGING OF THE PROXIMAL HUMERUS

S. Quayyum¹, C. Dickerson¹, M. Maly¹, G. Athwal², N. Knowles¹

¹University of Waterloo, Canada; ²The Roth McFarlane Hand and Upper Limb Centre, St. Joseph's Hospital, Canada

TOWARDS EXPLAINABLE PLACENTA PAS DETECTION USING DEEP ONE-CLASS CLASSIFICATION

G. Zhou¹, S. Coldman², X. Li^2,4, E. Whitby³, L. Guo^1,4

¹School of Electrical and Electronic Engineering, The University of Sheffield, UK; ²School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield, UK; ³School of Medicine and Population Health, The University of Sheffield, UK; ⁴INSIGNEO Institute, The University of Sheffield, UK

DEEP LEARNING BASED AUTOMATIC SEGMENTATION OF PLANCETA MRI

D. Wu¹, S. Coldman², X. Li^2,4, E. Whitby³, L. Guo^1,4

¹School of Electrical and Electronic Engineering, The University of Sheffield, UK; ²School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield, UK; ³School of Medicine and Population Health, The University of Sheffield, UK; ⁴INSIGNEO Institute, The University of Sheffield, UK

USING ARTIFICIAL INTELLIGENCE TO PREDICT POST-TREATMENT VISUAL ACUITY IN RETINAL VEIN OCCLUSION

G. Barbosa¹, E. Carvalho¹, A. Guerra¹, N. Ramião¹, M. Parente^1,2, S. Torres-Costa^3,4, M. Falcão^3,4

¹INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal; ²FEUP - Faculty of Engineering - University of Porto, Porto, Portugal; ³Department of Ophthalmology, São João University Hospital, 4200-437 Porto, Portugal;; ⁴FMUP - Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal

STATISTICAL DEFORMATION AND APPEARANCE MODEL OF TIBIA AND FIBULA INCLUDING GENERATED FRACTURES AND CALLUS

R. C. Reinardt, Y. Böhr, M. Roland, S. Diebels

Universität des Saarlandes,Applied Mechanics, Germany

ASSESSING CAROTID PLAQUE VULNERABILITY TO RUPTURE USING NON-INVASIVE IMAGING: AN EX VIVO EVALUATION OF MRI-QSM

F. Digeronimo^1,2,3, J. Bagnall^1,2, B. Tornifoglio^1,2, K. Shmueli⁴, C. Lally^1,2,3

¹Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland; ²Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin, Ireland; ³Advanced Materials and BioEngineering Research (AMBER) Centre, Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin, Ireland; ⁴Department of Medical Physics and Biomedical Engineering, University College London, London, UK