# Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative

## TL;DR

This paper introduces a fully automated, learning-based method for knee cartilage segmentation in MRI scans, utilizing hierarchical classifiers and a novel interaction technique to improve accuracy and efficiency in osteoarthritis research.

## Contribution

It presents a new hierarchical random forest approach combined with just-enough interaction for training data generation, enhancing segmentation accuracy in osteoarthritis MRI analysis.

## Key findings

- Significant reduction in segmentation errors compared to traditional methods
- Effective use of hierarchical classifiers and JEI for training data generation
- Validated on a large osteoarthritis MRI dataset from OAI

## Abstract

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.