Background: Computer-based three-dimensional (3D) visualizations reconstructed from sectional images represent a valuable tool in biomedical research and medical diagnosis. Particularly with those imaging techniques that provide virtual sections, such as CT, MRI, and CLSM, 3D reconstructions have become routine. Reconstructions from physical sections, such as those used in histological preparations, have not experienced an equivalent breakthrough, due to inherent shortcomings in sectional preparation that impede automated image-processing and reconstruction. The increased use of molecular techniques in morphological research, however, generates an overwhelming amount of 3D molecular information, stored within series of physical sections. This valuable information can be fully appreciated and interpreted only through an adequate method of 3D visualization.
Methods and results: In this paper we present a new method for a reliable and largely automated 3D reconstruction from physically sectioned material. The 'EMAC' concept (External Marker-based Automatic Congruencing) successfully approaches the three major obstacles to automated 3D reconstruction from serial physical sections: misalignment, distortion, and staining variation. It utilizes the objectivity of external markers for realignment of the sectional images and for geometric correction of distortion. A self-adapting dynamic thresholding technique compensates for artifactual staining variation and automatically selects the desired object contours.
Conclusions: Implemented on a low-cost hardware platform, EMAC provides a fast and efficient tool that largely facilitates the use of computer-based 3D visualization for the analysis of complex structural, molecular, and genetic information in morphological research. Due to its conceptual versatility, EMAC can be easily adapted for a broad range of tasks, including all modern molecular-staining techniques, such as immunohistochemistry and in situ hybridization.