We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.

In many neurophysiological studies, understanding the neuronal circuitry of the brain requires detailed 3D models of the nerve cells and their synapses. Typically, researchers build the 3D models by manually tracing the 2D cross-sectional profiles of the 3D structures from serial electron micrograph (EM) stacks and then construct the models from these 2D contours. While current computer-aided techniques can reduce the tracing time, they often require extensive user interaction. We propose a segmentation framework to extract the 2D profiles that is both fast and requires a minimal amount of user interaction. The framework uses graph cuts to minimize an energy defined over the image intensity and the flux of the intensity gradient field. Furthermore, to correct segmentation errors, our framework allows for efficient and intuitive editing of the initial results.

The expression levels of rod opsin and glial fibrillary acidic protein (GFAP) capture important structural changes in the retina during injury and recovery. Quantitatively measuring these expression levels in confocal micrographs requires identifying the retinal layer boundaries and spatially corresponding the layers across different images. In this paper, a method to segment the retinal layers using a parametric active contour model is presented. Then spatially aligned expression levels across different images are determined by thresholding the solution to a Dirichlet boundary value problem. Our analysis provides quantitative metrics of retinal restructuring that are needed for improving retinal therapies after injury.