一种基于马尔可夫聚类的特征选择策略,用于从MRI数据中优化脑肿瘤分割

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS
Ioan-Marius Pisak-Lukáts, L. Kovács, Szilágyi László
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引用次数: 0

摘要

医学图像的自动分割是现代医学诊断、治疗计划和干预后随访研究的基础。分割的准确性是协助医生工作的关键因素,但该过程的效率也是相关的。本文介绍了一种特征选择策略,该策略试图为基于MRI数据的脑肿瘤分割中使用的集成学习方法定义约简特征集,从而使分割结果几乎不受任何损害。最初,完整的观察和生成的特征集被部署在测试数据的集成训练和预测中,这为我们提供了来自完整特征集的所有特征对的信息。将提取的成对数据输入到马尔可夫聚类(MCL)算法中,该算法使用图结构来表征特征之间的关系。MCL产生相互完全分离的连通子图。最大的子图定义了选择用于评估的特征组。在基于二叉决策树的集成学习框架中,使用BraTS 2019挑战赛训练数据集的高级别和低级别肿瘤记录对所提出的技术进行了评估。该方法可以在不影响分割精度的前提下将特征集缩小到初始大小的30%,显著提高了分割过程的效率。提供了完整的104个特征集和简化的41个特征集的详细比较,特别注意MRI数据中的高度判别和冗余特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A feature selection strategy using Markov clustering, for the optimization of brain tumor segmentation from MRI data
Abstract The automatic segmentation of medical images stands at the basis of modern medical diagnosis, therapy planning and follow-up studies after interventions. The accuracy of the segmentation is a key element in assisting the work of the physician, but the efficiency of the process is also relevant. This paper introduces a feature selection strategy that attempts to define reduced feature sets for ensemble learning methods employed in brain tumor segmentation based on MRI data such a way that the segmentation outcome hardly suffers any damage. Initially, the full set of observed and generated features are deployed in ensemble training and prediction on testing data, which provide us information on all couples of features from the full feature set. The extracted pairwise data is fed to a Markov clustering (MCL) algorithm, which uses a graph structure to characterize the relation between features. MCL produces connected subgraphs that are totally separated from each other. The largest such subgraph defines the group of features which are selected for evaluation. The proposed technique is evaluated using the high-grade and low-grade tumor records of the training dataset of the BraTS 2019 challenge, in an ensemble learning framework relying on binary decision trees. The proposed method can reduce the set of features to 30%ofits initial size without losing anything in terms of segmentation accuracy, significantly contributing to the efficiency of the segmentation process. A detailed comparison of the full set of 104 features and the reduced set of 41 features is provided, with special attention to highly discriminative and redundant features within the MRI data.
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来源期刊
Acta Universitatis Sapientiae Informatica
Acta Universitatis Sapientiae Informatica COMPUTER SCIENCE, THEORY & METHODS-
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