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Proceedings Paper

Design and validation of biologically inspired spiculated breast lesion models utilizing structural tissue distortion
Author(s): Premkumar Elangovan; Elena Mihalas; Majdi Alnowami; Kenneth C. Young; David R. Dance; Victoria Cooke; Louise Wilkinson; Rosalind M. Given-Wilson; Matthew G. Wallis; Kevin Wells
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Paper Abstract

The use of conventional clinical trials to optimise technology and techniques in breast cancer screening carries with it issues of dose, high cost and delay. This has motivated the development of Virtual Clinical Trials (VCTs) as an alternative in-silico assessment paradigm. However, such an approach requires a set of modelling tools that can realistically represent the key biological and technical components within the imaging chain. The OPTIMAM image simulation toolbox provides a complete validated end-to-end solution for VCTs, wherein commonly-found regular and irregular lesions can be successfully and realistically simulated. As spiculated lesions are the second most common form of solid mass we report on our latest developments to produce realistic spiculated lesion models, with particular application in Alternative Forced Choice trials. We make use of sets of spicules drawn using manually annotated landmarks and interpolated by a fitted 3D spline for each spicule. Once combined with a solid core, these are inserted into 2D and tomosynthesis image segments and blended using a combination of elongation, rotational alignment with background, spicule twisting and core radial contraction effects. A mixture of real and simulated images (86 2D and 86 DBT images) with spiculated lesions were presented to an experienced radiologist in an observer study. The latest observer study results demonstrated that 88.4% of simulated images of lesions in 2D and 67.4% of simulated lesions in DBT were rated as definitely or probably real on a six-point scale. This presents a significant improvement on our previous work which did not employ any background blending algorithms to simulate spiculated lesions in clinical images.

Paper Details

Date Published: 9 March 2018
PDF: 11 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730B (9 March 2018); doi: 10.1117/12.2293421
Show Author Affiliations
Premkumar Elangovan, Univ. of Surrey (United Kingdom)
Royal Surrey County Hospital (United Kingdom)
Elena Mihalas, Univ. of Surrey (United Kingdom)
Majdi Alnowami, Univ. of Surrey (United Kingdom)
King Abdulaziz Univ. (Saudi Arabia)
Kenneth C. Young, Royal Surrey County Hospital (United Kingdom)
Univ. of Surrey (United Kingdom)
David R. Dance, Royal Surrey County Hospital (United Kingdom)
Univ. of Surrey (United Kingdom)
Victoria Cooke, Jarvis Breast Screening and Diagnostic Ctr. (United Kingdom)
Louise Wilkinson, Oxford Univ. Hospitals NHS Trust (United Kingdom)
Rosalind M. Given-Wilson, St George's Healthcare NHS Trust (United Kingdom)
Matthew G. Wallis, Cambridge Univ. Hospitals NHS Foundation Trust (United Kingdom)
NIHR Cambridge Biomedical Research Ctr. (United Kingdom)
Kevin Wells, Univ. of Surrey (United Kingdom)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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