Robust methods for segmentation and tracking are critical for quantitative biology. We are developing methods for accurate, automated and interactive 2D/3D segmentation of cellular and sub-cellular structures, with emphasis on high-throughput image analysis. The three main issues that make analyzing microscopic imagery challenging are modeling of cell appearance which varies from one dataset to another, poor signal to noise ratio/clutter, and the large scale nature of the datasets. We have developed interactive static image segmentation methods using Markov Random Fields (MRFs) that utilize a human in the loop to achieve accurate segmentation. Further, we have proposed an approach for parameter adaptation that induces generic high-level priors on topology, facilitating robust tracing of neuronal structures. Recent work focuses on modeling edge appearance and scaling tracing techniques to hundreds of structures. Finally, we have also focused on the problem of detecting deformable structures such as synaptic junctions in Electron Micrographs.