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Simulation of sequential pathology images for the virtual clinical trials with rad-path correlation
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Paper Abstract

A simulation of sequential breast pathology images is proposed, as a prerequisite for the development of virtual clinical trials (VCTs) with radiology-pathology (rad-path) correlation. The rad-path correlation of breast cancer findings is performed clinically to confirm concordance and increase confidence in diagnoses. VCTs have been used for optimization of breast imaging systems, based upon computer simulation of breast anatomy, imaging modalities, and image interpretation. Today, VCTs are used to optimize breast imaging at the “radiology” spatial scale, by simulating tissue structures seen in radiological images, namely, skin, adipose or dense tissue compartments, fibrous ligaments, and major ducts and blood vessels. We have extended this simulation to the microscopic (i.e., “pathology”) spatial scale, to allow for virtual rad-path correlation. Previously, we developed a manual simulation of adipose and dense tissue regions in pathology images, corresponding to a small region selected within a breast phantom simulated at the radiological scale. In this paper, we describe an automated simulation of adipocytes, epithelial and myoepithelial cells, collagen fibers, and fibroblasts. Adipocytes are simulated by recursive partitioning. Epithelial and myoepithelial cells are simulated radially around ductal or acinar lumen. Fibers and fibroblasts are simulated by an analogy with the electrostatic field. Our approach models the volumetric distributions of cells and various breast tissues, which allows the simulation of sequential pathology images at clinical inter-slice distances. The proposed simulation method has been evaluated by a clinical pathologist and medical physicists. The effect of the simulation approaches on the visual appearance of simulated pathology images has been evaluated.

Paper Details

Date Published: 6 July 2018
PDF: 8 pages
Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107180F (6 July 2018); doi: 10.1117/12.2318492
Show Author Affiliations
Predrag R. Bakic, Univ. of Pennsylvania (United States)
David D. Pokrajac, Univ. of Pennsylvania (United States)
Delaware State Univ. (United States)
Michael D. Feldman, Univ. of Pennsylvania (United States)
Andrew D. A. Maidment, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10718:
14th International Workshop on Breast Imaging (IWBI 2018)
Elizabeth A. Krupinski, Editor(s)

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