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Proceedings Paper

3D prostate histology reconstruction: an evaluation of image-based and fiducial-based algorithms
Author(s): Eli Gibson; Mena Gaed; José A. Gómez; Madeleine Moussa; Cesare Romagnoli; Joseph L. Chin; Cathie Crukley; Glenn S. Bauman; Aaron Fenster; Aaron D. Ward
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Paper Abstract

Imaging may enable the determination of the spatial distribution and aggressiveness of prostate cancer in vivo before treatment, possibly supporting diagnosis, therapy selection, and focal therapy guidance. 3D reconstruction of prostate histology facilitates the validation of such imaging applications. We evaluated four histology–ex vivo magnetic resonance (MR) image 3D reconstruction algorithms comprising two similarity metrics (mutual information MMI or fiducial registration error MFRE) and two search domains (affine transformations TA or fiducial-constrained affine transformations TF). Seven radical prostatectomy specimens were imaged with MR imaging, processed for whole-mount histology, and digitized as histology images. The algorithms were evaluated on the reconstruction error and the sensitivity of same to translational and rotational errors in initialization. Reconstruction error was quantified as the target registration error (TRE): the post-reconstruction distance between homologous point landmarks (7–15 per histology section; 132 total) identified on histology and MR images. Sensitivity to initialization was quantified using a linear model relating TRE to varied levels of translational/rotational initialization errors. The algorithm using MMI and TA yielded a mean TRE of 1.2±0.7 mm when initialized using an approach that assumes histology corresponds to the front faces of tissue blocks, but was sensitive to initialization error. The algorithm using MFRE and TA yielded a mean TRE of 0.8±0.4 mm with minimal sensitivity to initialization errors. Compared to the method used to initialize the algorithms (mean TRE 1.4±0.7 mm), a study using an algorithm with a mean TRE of 0.8 mm would require 27% fewer subjects for certain imaging validation study designs.

Paper Details

Date Published: 29 March 2013
PDF: 7 pages
Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760A (29 March 2013); doi: 10.1117/12.2006897
Show Author Affiliations
Eli Gibson, The Univ. of Western Ontario (Canada)
Mena Gaed, The Univ. of Western Ontario (Canada)
José A. Gómez, The Univ. of Western Ontario (Canada)
Madeleine Moussa, The Univ. of Western Ontario (Canada)
Cesare Romagnoli, The Univ. of Western Ontario (Canada)
Joseph L. Chin, The Univ. of Western Ontario (Canada)
Cathie Crukley, The Univ. of Western Ontario (Canada)
Glenn S. Bauman, The Univ. of Western Ontario (Canada)
Aaron Fenster, The Univ. of Western Ontario (Canada)
Aaron D. Ward, The Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 8676:
Medical Imaging 2013: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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