Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative.
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative.
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DOI：
10.1007/978-3-319-46723-8_40
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发表时间：
2016-10-01
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影响因子：
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通讯作者：
Sonka, Milan
中科院分区：
文献类型：
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作者：
Kashyap, Satyananda;Oguz, Ipek;Sonka, Milan
我们提出了一种完全自动化的基于学习的方法，用于在存在骨关节炎(OA)的情况下分割膝关节软骨。该算法采用由两个随机森林分类器组成的分层集合。第一种是邻域近似森林，其输出概率图被用作第二种随机森林分类器的特征集。分层方法的输出概率被用作多个对象和表面的分层最优图形分割(LOGISMOS)的代价函数。在这项工作中，我们强调了一种新的后处理交互，称为刚好足够的交互(JEI)，它能够快速而准确地生成大量训练样本。15名和13名不相交的受试者被用于训练，并在另一组53个不相交的膝关节数据集上进行测试。所有图像均使用双回波稳态(DESS)MRI序列采集，来自骨关节炎倡议(OAI)数据库。使用基于学习的代价函数的分割性能显示，与传统的基于梯度的代价函数相比，分割误差显著降低(p<；0.05)。
We present a fully automated learning-based approach for segmenting knee cartilage in 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 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.