Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
This work addresses the problem of accurate and efficient knee cartilage segmentation for osteoarthritis diagnosis, though it is incremental as it builds on existing methods like random forests and LOGISMOS.
The researchers tackled automated knee cartilage segmentation in osteoarthritis MRI by developing a hierarchical random forest classifier combined with LOGISMOS, resulting in a significant reduction in segmentation errors (p < 0.05) compared to gradient-based methods.
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.