  We present a fully automated learning-based approach for segmenting knee
cartilage in the presence of osteoarthritis (OA). The algorithm employs a
hierarchical set of two random forest classifiers. The first is a neighborhood
approximation forest, the output probability map of which is utilized as a
feature set for the second random forest (RF) classifier. The output
probabilities of the hierarchical approach are used as cost functions in a
Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS).
In this work, we highlight a novel post-processing interaction called
just-enough interaction (JEI) which enables quick and accurate generation of a
large set of training examples. Disjoint sets of 15 and 13 subjects were used
for training and tested on another disjoint set of 53 knee datasets. All images
were acquired using a double echo steady state (DESS) MRI sequence and are from
the osteoarthritis initiative (OAI) database. Segmentation performance using
the learning-based cost function showed significant reduction in segmentation
errors ($p< 0.05$) in comparison with conventional gradient-based cost
functions.
