We introduce a model-based approach for segmenting and quantifying GFP-tagged subcellular structures of the Golgi apparatus in 2D and 3D microscopy images. The approach is based on 2D and 3D intensity models, which are directly fitted to an image within 2D circular or 3D spherical regions-of-interest (ROIs). We also propose automatic approaches for the detection of candidates, for the initialization of the model parameters, and for adapting the size of the ROI used for model fitting. Based on the fitting results, we determine statistical information about the spatial distribution and the total amount of intensity (fluorescence) of the subcellular structures. We demonstrate the applicability of our new approach based on 2D and 3D microscopy images.
Vascular factors associated with Alzheimer's disease (AD) have recently gained increased attention. To investigate changes in vascular, particularly microvascular architecture, we developed a hierarchical imaging framework to obtain large-volume, high-resolution 3D images from brains of transgenic mice modeling AD. In this paper, we present imaging and data analysis methods which allow compiling unique characteristics from several hundred gigabytes of image data. Image acquisition is based on desktop micro-computed tomography (µCT) and local synchrotron-radiation µCT (SRµCT) scanning with a nominal voxel size of 16 µm and 1.4 µm, respectively. Two visualization approaches were implemented: stacks of Z-buffer projections for fast data browsing, and progressive-mesh based surface rendering for detailed 3D visualization of the large datasets. In a first step, image data was assessed visually via a Java client connected to a central database. Identified characteristics of interest were subsequently quantified using global morphometry software. To obtain even deeper insight into microvascular alterations, tree analysis software was developed providing local morphometric parameters such as number of vessel segments or vessel tortuosity. In the context of ever increasing image resolution and large datasets, computer-aided analysis has proven both powerful and indispensable. The hierarchical approach maintains the context of local phenomena, while proper visualization and morphometry provide the basis for detailed analysis of the pathology related to structure. Beyond analysis of microvascular changes in AD this framework will have significant impact considering that vascular changes are involved in other neurodegenerative diseases as well as in cancer, cardiovascular disease, asthma, and arthritis.
A wide range of disorders are associated with alterations of the central and peripheral vascular system. Modified vascular corrosion casting using a newly developed polymer, allows for the first time hierarchical assessment of 3D vessel data in animals down to the level of capillaries. Imaging of large volumes of vasculature at intermediate resolution (16 μm) was performed using a desktop micro-computed tomography system. Subsequently regions of interest were identified for additional high resolution imaging (1.4 μm) at the X-ray Tomographic Microscopy (XTM) station of the Swiss Light Source (SLS). A framework for systematic hierarchical imaging and quantification was developed. Issues addressed included enhanced XTM data acquisition, introduction of local tomography, sample navigation, advanced post processing, and data combination. In addition to visual assessment of qualitative changes, morphometrical and architectural indices were determined using direct 3D morphometry software developed in house. Vessel specific parameters included thickness, surface, connectivity, and vessel length. Reconstructions of cerebral vasculature in mutant mice modeling Alzheimer's disease revealed significant changes in vessel architecture and morphology. In the future, a combination of these techniques may support drug discovery. Additionally, future ultra-high-resolution in vivo systems may even allow non-invasive tracking of temporal alterations in vascular morphology.