Streicher J, Weninger W J, Müller G B
Department of Anatomy, University of Vienna, Wien, Austria.
Anat Rec. 1997 Aug;248(4):583-602. doi: 10.1002/(SICI)1097-0185(199708)248:4<583::AID-AR10>3.0.CO;2-L.
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.
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.
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.
从断层图像重建的基于计算机的三维（3D）可视化技术是生物医学研究和医学诊断中的一项重要工具。特别是对于那些能提供虚拟切片的成像技术，如CT、MRI和CLSM，3D重建已成为常规操作。而从物理切片（如组织学制备中使用的切片）进行的重建，由于切片制备中存在妨碍自动图像处理和重建的固有缺陷，尚未取得同等程度的突破。然而，形态学研究中分子技术的广泛应用产生了大量存储在一系列物理切片中的3D分子信息。只有通过适当的3D可视化方法才能充分理解和解读这些宝贵信息。
在本文中，我们提出了一种从物理切片材料进行可靠且基本自动化的3D重建的新方法。“EMAC”概念（基于外部标记的自动匹配）成功克服了从连续物理切片进行自动化3D重建的三大障碍：错位、变形和染色变化。它利用外部标记的客观性来重新对齐断层图像并对变形进行几何校正。一种自适应动态阈值技术可补偿人为染色变化并自动选择所需的物体轮廓。
EMAC在低成本硬件平台上实现，提供了一种快速高效的工具，极大地促进了基于计算机的3D可视化技术在形态学研究中对复杂结构、分子和遗传信息分析的应用。由于其概念的通用性，EMAC可轻松适用于广泛的任务，包括所有现代分子染色技术，如免疫组织化学和原位杂交。
