Toward routine use of 3D histopathology as a research tool

Abstract

Three-dimensional (3D) reconstruction and examination of tissue at microscopic resolution have significant potential to enhance the study of both normal and disease processes, particularly those involving structural changes or those in which the spatial relationship of disease features is important. Although other methods exist for studying tissue in 3D, using conventional histopathological features has significant advantages because it allows for conventional histopathological staining and interpretation techniques. Until now, its use has not been routine in research because of the technical difficulty in constructing 3D tissue models. We describe a novel system for 3D histological reconstruction, integrating whole-slide imaging (virtual slides), image serving, registration, and visualization into one user-friendly package. It produces high-resolution 3D reconstructions with minimal user interaction and can be used in a histopathological laboratory without input from computing specialists. It uses a novel method for slice-to-slice image registration using automatic registration algorithms custom designed for both virtual slides and histopathological images. This system has been applied to >300 separate 3D volumes from eight different tissue types, using a total of 5500 virtual slides comprising 1.45 TB of primary image data. Qualitative and quantitative metrics for the accuracy of 3D reconstruction are provided, with measured registration accuracy approaching 120 μm for a 1-cm piece of tissue. Both 3D tissue volumes and generated 3D models are presented for four demonstrator cases.

Figure 1

Examples of original images (axial views) from data sets: an 18-day mouse embryo stained with H&E (A), human liver containing a deposit of metastatic colorectal carcinoma stained with H&E (B), human liver with hepatitis C stained with picrosirius red (C), and a single rat glomerulus stained with solochrome cyanin/phloxin (D).

Figure 2

Performing multilevel registration at successive image magnifications using interactive subimage selection.

Figure 3

A: Coronal view through the center of an image stack (Figure 1A) showing failures in rigid registration. B: Coronal view through the center of the same image stack (data set B, liver with metastatic colorectal carcinoma) showing reconstruction has worked successfully after subsequent manual correction and nonrigid registration.

Figure 4

A: Axial view of cirrhotic liver tissue stained with H&E with highlighted synthetic holes used for registration accuracy metrics. B: Axial view of cirrhotic liver tissue stained with picrosirius red with a central single blood vessel used for registration accuracy metrics.

Figure 5

Axial, coronal, and sagittal views with a 3D volume rendition and 3D visualization of anatomical features of the mouse embryo (A), metastatic colorectal carcinoma in human liver tissue (B), cirrhotic human liver tissue infected with hepatitis C (C), and a single rat glomerulus (D). In B, the tumor is marked in red and the blood vessels in the adjacent liver tissue are marked in yellow. Colors in the first three columns are original colors of the stained tissue. Colors in the last column are pseudocolors chosen to highlight differences between tissue types.