In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there
is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects"
within these images. These are challenging problems for image processing because of the variability in object appearance
that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to
address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a
human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool
in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have
tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between
images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools
that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the
need for operator input over time, and can potentially provide a more dynamic balance between customization and
automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these
algorithms, and compares the segmentation performance of various design choices.