Feasibility analysis of high resolution tissue image registration using 3-D synthetic data

Abstract

Background: Registration of high-resolution tissue images is a critical step in the 3D analysis of protein expression. Because the distance between images (~4-5μm thickness of a tissue section) is nearly the size of the objects of interest (~10-20μm cancer cell nucleus), a given object is often not present in both of two adjacent images. Without consistent correspondence of objects between images, registration becomes a difficult task. This work assesses the feasibility of current registration techniques for such images.

Methods: We generated high resolution synthetic 3-D image data sets emulating the constraints in real data. We applied multiple registration methods to the synthetic image data sets and assessed the registration performance of three techniques (i.e., mutual information (MI), kernel density estimate (KDE) method [1], and principal component analysis (PCA)) at various slice thicknesses (with increments of 1μm) in order to quantify the limitations of each method.

Results: Our analysis shows that PCA, when combined with the KDE method based on nuclei centers, aligns images corresponding to 5μm thick sections with acceptable accuracy. We also note that registration error increases rapidly with increasing distance between images, and that the choice of feature points which are conserved between slices improves performance.

Conclusions: We used simulation to help select appropriate features and methods for image registration by estimating best-case-scenario errors for given data constraints in histological images. The results of this study suggest that much of the difficulty of stained tissue registration can be reduced to the problem of accurately identifying feature points, such as the center of nuclei.

Keywords: 3-D Tissue Image Registration; Cancer Heterogeneity Analysis; Kernel Density; Tissue Image Processing.

Figure 1

(a-b) Sample images for two adjacent slices stained with quantum dots. Note the ambiguity in correspondences in the two marked regions; (c) DAPI stained image corresponding to (b); (d) QD stained slice 25μm (5 sections) apart from slice (b). Note the change in acini shape between (b) and (d)

Figure 2

Simulation pipeline for evaluation of registration methods

Figure 3

(a): Synthetic volume with spherical cells arranged around a cylindrical acinus. (b): A close-up region of the volume. (c): A typical slice of the volume. (d): Adjacent slice at a z-depth of 1μm from slice in (c). (e): A slice at z-depth of 5μm (typical section thickness in real slices) from slice in (c). Note the changing number of cells and their sizes in (c), (d) and (e)

Figure 4

Overlaid display of the two images 5μm apart for best variant of each method. (a): Ground truth (aligned images). (b): Randomly transformed images. (c): PCA alignment with nuclei centers. (d): MI with PCA alignment (nuclei centers) and images filtered at optimal σ_f. (e): KDE with PCA alignment (nuclei centers) and optimal σ_k. Note the increasing improvement in cell alignment (yellow vertical arrow pointing to a single cell) from (c) to (e). KDE initialized with PCA gives the best results in (e). Also note the overlap between cyan and red arrows in the center indicating progressive improvement in alignment

Figure 5

Slice thickness (ST) versus mean RMSE for various methods. Left: Type 00 data. Right: Type 11 data