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3D prostate histology reconstruction: An evaluation of image‐based and fiducial‐based algorithms
Author(s) -
Gibson E.,
Gaed M.,
Gómez J. A.,
Moussa M.,
Romagnoli C.,
Pautler S.,
Chin J. L.,
Crukley C.,
Bauman G. S.,
Fenster A.,
Ward A. D.
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4816946
Subject(s) - fiducial marker , image registration , artificial intelligence , initialization , computer science , algorithm , gold standard (test) , prostate , magnetic resonance imaging , prostatectomy , affine transformation , medical imaging , computer vision , medicine , mathematics , radiology , image (mathematics) , cancer , pure mathematics , programming language
Purpose: Evaluation of in vivo prostate imaging modalities for determining the spatial distribution and aggressiveness of prostate cancer ideally requires accurate registration of images to an accepted reference standard, such as histopathological examination of radical prostatectomy specimens. Three‐dimensional (3D) reconstruction of prostate histology facilitates these registration‐based evaluations by reintroducing 3D spatial information lost during histology processing. Because the reconstruction accuracy may constrain the clinical questions that can be answered with these data, it is important to assess the tradeoffs between minimally disruptive methods based on intrinsic image information and potentially more robust methods based on extrinsic fiducial markers.Methods: Ex vivo magnetic resonance (MR) images and digitized whole‐mount histology images from 12 radical prostatectomy specimens were used to evaluate four 3D histology reconstruction algorithms. 3D reconstructions were computed by registering each histology image to the corresponding ex vivo MR image using one of two similarity metrics (mutual information or fiducial registration error) and one of two search domains (affine transformations or a constrained subset thereof). The algorithms were evaluated for accuracy using the mean target registration error (TRE) computed from homologous intrinsic point landmarks (3–16 per histology section; 232 total) identified on histology and MR images, and for the sensitivity of TRE to rotational, translational, and scaling initialization errors.Results: The algorithms using fiducial registration error and mutual information had mean ± standard deviation TREs of 0.7 ± 0.4 and 1.2 ± 0.7 mm, respectively, and one algorithm using fiducial registration error and affine transforms had negligible sensitivities to initialization errors. The postoptimization values of the mutual information‐based metric showed evidence of errors due to both the optimizer and the similarity metric, and variation of parameters of the mutual information‐based metric did not improve its performance.Conclusions: The extrinsic fiducial‐based algorithm had lower mean TRE and lower sensitivity to initialization than the intrinsic intensity‐based algorithm using mutual information. A model relating statistical power to registration error for certain imaging validation study designs estimated that a reconstruction algorithm with a mean TRE of 0.7 mm would require 27% fewer subjects than the method used to initialize the algorithms (mean TRE 1.3 ± 0.7 mm), suggesting the choice of reconstruction technique can have a substantial impact on the design of imaging validation studies, and on their overall cost.