A general discrete contour model in two, three, and four dimensions for topology-adaptive multichannel segmentation

We present a discrete contour model for the segmentation of image data with any dimension of image domain and value range. The model consists of a representation using simplex meshes and a mechanical formulation of influences that drive an iterative segmentation. The object's representation as well as the influences are valid for any dimension of the image domain. The image influences introduced here, can combine information from independent channels of higher-dimensional value ranges. Additionally, the topology of the model automatically adapts to objects contained in images. Noncontextual tests have validated the ability of the model to reproducibly delineate synthetic objects. In particular, images with a signal to noise ratio of SNR /spl les/ 0.5 are delineated within two pixels of their ground truth contour. Contextual validations have shown the applicability of the model for medical image analysis in image domains of two, three, and four dimensions in single as well as multichannel value ranges.     

Computer-assisted quantification of axo-somatic boutons at the cell membrane of motoneurons

This paper presents a system for computer-assisted quantification of axo-somatic boutons at motoneuron cell-surface membranes. Different immunohistochemical stains can be used to prepare tissue of the spinal cord. Based on micrographs displaying single neurons, a finite element balloon model has been applied to determine the exact location of the cell membrane. A synaptic profile is extracted next to the cell membrane and normalized with reference to the intracellular brightness. Furthermore, a manually selected reference cell is used to normalize settings of the microscope as well as variations in histochemical processing for each stain. Thereafter, staining, homogeneity, and allocation of boutons are determined automatically from the synaptic profiles. The system is evaluated by applying the coefficient of variation (Cv) to repeated measurements of a quantity. Based on 1856 motoneuronal images acquired from four animals with three stains, 93% of the images are analyzed correctly. The others were rejected, based on process protocols. Using only rabbit anti-synaptophysin as primary antibody, the correctness increases above 96%. Cv values are below 3%, 5%, and 6% for all measures with respect to stochastic optimization, cell positioning, and a large range of microscope settings, respectively. A sample size of about 100 is required to validate a significant reduction of staining in motoneurons below a hemi-section (Wilcoxon rank-sum test, alpha = 0.05, beta = 0.9). Our system yields statistically robust results from light micrographs. In future, it is hoped that this system will substitute for the expensive and time-consuming analysis of spinal cord injury at the ultra-structural level, such as by manual interpretation of nonoverlapping electron micrographs.