A major goal in neuroanatomy is to obtain precise information about the functional organization of neuronal assemblies and their interconnections. Therefore, the analysis of histological sections frequently requires high resolution images in combination with an overview about the structure. To overcome this conflict we have previously introduced a software for the automatic acquisition of multiple image stacks (3D-MISA) in confocal laser scanning microscopy. Here, we describe a Windows NT based software for fast and easy navigation through the multiple images stacks (MIS-browser), the visualization of individual channels and layers and the selection of user defined subregions. In addition, the MIS browser provides useful tools for the visualization and evaluation of the datavolume, as for instance brightness and contrast corrections of individual layers and channels. Moreover, it includes a maximum intensity projection, panning and zoom in/out functions within selected channels or focal planes (x/y) and tracking along the z-axis. The import module accepts any tiff-format and reconstructs the original image arrangement after the user has defined the sequence of images in x/y and z and the number of channels. The implemented export module allows storage of user defined subregions (new single image stacks) for further 3D-reconstruction and evaluation.
Image acquisition at high magnification is inevitably correlated with a limited view over the entire tissue section. To overcome this limitation we designed software for multiple image-stack acquisition (3D-MISA) in confocal laser scanning microscopy (CLSM). The system consists of a 4 channel Leica CLSM equipped with a high resolution z- scanning stage mounted on a xy-monitorized stage. The 3D- MISA software is implemented into the microscope scanning software and uses the microscope settings for the movements of the xy-stage. It allows storage and recall of 70 xyz- positions and the automatic 3D-scanning of image arrays between selected xyz-coordinates. The number of images within one array is limited only by the amount of disk space or memory available. Although for most applications the accuracy of the xy-scanning stage is sufficient for a precise alignment of tiled views, the software provides the possibility of an adjustable overlap between two image stacks by shifting the moving steps of the xy-scanning stage. After scanning a tiled image gallery of the extended focus-images of each channel will be displayed on a graphic monitor. In addition, a tiled image gallery of individual focal planes can be created. In summary, the 3D-MISA allows 3D-image acquisition of coherent regions in combination with high resolution of single images.
Confocal microscopy is qualified to perform volume scans of nerve cells with dendrites and spines. Length and diameter of the dendrite branches and the spines should be determined to analyze the influence of learning processes. A prerequisite for that is the recognition of the dendritic structure with branching off and spines. Because the microscope operates at the resolution limit the images are blurry, noisy and only poorly sampled. In contrast to other methods which are based on binary images and thinning algorithms, our method tracks and dendritic tree faster and in the gray-level domain using simple geometric models.An explicit segmentation is unnecessary and knowledge about shape and structure of the dendrite is included as a priori information. For large trees, first a low resolution scan is captured to crete a rough model. The algorithm allows to refine this model using higher resolution scans for interesting regions along the dendrite. The large unimportant areas between the dendrite branches are not scanned at high resolution to save time and disc space. In a second step, the parameters of the model are adapted to the microscope image by minim zing the deviation of the microscope image from the mode image convolved by the microscope point spread function. Features like number, diameter, length and position of the dendrite branches and spines can be easily calculated from the model. An interactive user intervention is possible at the model domain.
For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. For a more detailed analysis, such as the classification of spine types, binary images of higher resolution are required. Simple threshold methods have disadvantages for small structures because the microscope point spread function (PSF) causes a darkening and a spread. The direction-dependent PSF leads to shape errors. To reconstruct structures and edge positions with a resolution smaller than one voxel a parametric model for the dendrite and the spines is created. In our application we use the known tree-like structure of the nerve cell as a- priori information. To create the model, simple geometrical elements (cylinders with hemispheres at the ends) are connected. The model can be adapted for size and position in sub-pixel domain. To estimate the quadratic error between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope PSF. During an iterative process the parameters of the model are optimized. In contrast to other pixel-based methods. the number of variable parameters is much slower. The influence of small deviations in the microscope image (caused by the inhomogeneous biological materials) is reduced.
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