Comparative analysis of tissue reconstruction algorithms for 3D histology

Abstract

Motivation: Digital 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.

Results: We 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 implementation: Code: 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.

Fig. 1.

Evaluation framework. A series of tissue images is input to a reconstruction method for registration. The transformations estimated by the method are re-applied to masks defining the tissue region and images containing landmarks. The registered tissue, mask and landmark images are used to evaluate reconstruction accuracy based on numerical metrics and visual examination. Moreover, tunable settings can be optimized. (Color version of this figure is available at Bioinformatics online.)

Fig. 2.

Reconstructions using (a) LS based on landmarks by observer 1, (b) OPT, (c) SIFT, (d) HSR, (e) RVSS, (f) ESA, (g) MIM and (h) Voloom. Optimized parameters and the most suitable resolution were used for each method. The dots represent the trajectory of accumulated target registration error from section to section. The horizontal lines indicate the direction and magnitude of the cumulative mean displacement of each section relative to the ideal error-free trajectory (vertical line). Magnified views are shown next to each reconstruction. Viewing the high-resolution color version of the Figure online is recommended. (Color version of this figure is available at Bioinformatics online.)

Fig. 3.

Reconstructions using (a) LS based on landmarks by observer 1, (b) OPT, (c) SIFT, (d) HSR, (e) RVSS, (f) ESA, (g) MIM and (h) Voloom. Optimized parameters and the most suitable resolution were used for each method. The locations of the four landmark points on each section are indicated with dots, shown together with lines of best fit to each of the four series of points. Note that the scale of the vertical axis is different from the horizontal axes in the visualization. Viewing the high-resolution color version of the Figure online is recommended. (Color version of this figure is available at Bioinformatics online.)