Abstract
MotivationDigital pathology enables new approaches that expand beyond storage, visualization or analysis of histological samples in digital format. One novel opportunity is 3D histology, where a three-dimensional reconstruction of the sample is formed computationally based on serial tissue sections. This allows examining tissue architecture in 3D, for example, for diagnostic purposes. Importantly, 3D histology enables joint mapping of cellular morphology with spatially resolved omics data in the true 3D context of the tissue at microscopic resolution. Several algorithms have been proposed for the reconstruction task, but a quantitative comparison of their accuracy is lacking.ResultsWe developed a benchmarking framework to evaluate the accuracy of several free and commercial 3D reconstruction methods using two whole slide image datasets. The results provide a solid basis for further development and application of 3D histology algorithms and indicate that methods capable of compensating for local tissue deformation are superior to simpler approaches.Availability and implementationCode: https://github.com/BioimageInformaticsTampere/RegBenchmark. Whole slide image datasets: http://urn.fi/urn: nbn: fi: csc-kata20170705131652639702.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Digitalization of pathology has been accelerated by improvements in technology allowing acquisition of whole slide images (WSI) (Ghaznavi et al, 2013; Griffin and Treanor, 2017)
root mean squared error (RMSE), normalized cross correlation (NCC), mutual information (MI), normalized mutual information (NMI), f2 and f3 depend both on resolution and image content, and these metrics should only be compared within the same dataset and resolution
Methods utilizing locally varying transformations (ESA, Medical Image Manager (MIM), RVSS, Voloom) were superior to those constrained to global affine models (OPT, Scale Invariant Feature Transform (SIFT), HSR)
Summary
Digitalization of pathology has been accelerated by improvements in technology allowing acquisition of whole slide images (WSI) (Ghaznavi et al, 2013; Griffin and Treanor, 2017). Examples of potential applications include construction of data-driven computer models and improved diagnostics of diseases associated with changes in the 3D microarchitecture of tissue. 3D histology is compatible with established histopathological interpretation techniques and biochemical assays such as immunohistochemistry or in situ hybridization. This raises interesting prospects in view of recent advances in spatially resolved omics (Mignardi et al, 2017; Stahl et al, 2016). Epigenomic, transcriptomic and proteomic data in the spatial context of tissue holds great promise for pathology and other fields (Koos et al, 2015).
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