Simulation of ground-truth validation data via physically- and statistically-based warps.

The problem of scarcity of ground-truth expert delineations of medical image data is a serious one that impedes the training and validation of medical image analysis techniques. We develop an algorithm for the automatic generation of large databases of annotated images from a single reference dataset. We provide a web-based interface through which the users can upload a reference data set (an image and its corresponding segmentation and landmark points), provide custom setting of parameters, and, following server-side computations, generate and download an arbitrary number of novel ground-truth data, including segmentations, displacement vector fields, intensity non-uniformity maps, and point correspondences. To produce realistic simulated data, we use variational (statistically-based) and vibrational (physically-based) spatial deformations, nonlinear radiometric warps mimicking imaging nonhomogeneity, and additive random noise with different underlying distributions. We outline the algorithmic details, present sample results, and provide the web address to readers for immediate evaluation and usage.