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A Comparative Study of Two Prediction Models for Brain Tumor Progression

机译：两种脑肿瘤进展预测模型的比较研究

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摘要

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named "Dropout" can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region.
机译：MR扩散张量成像（DTI）技术与传统的T1或T2加权MRI扫描相结合，为脑癌诊断提供了丰富的信息来源。这些图像形成大规模的高维数据集。由于这些图像之间存在显着的相关性，因此我们假设低维几何数据结构（流形）被嵌入到高维空间中。那些歧管可能对放射科医生来说是隐藏的，因为对于人类专家而言，解释高维数据具有挑战性。歧管的识别是成功分析多模态MR图像的关键步骤。我们已经开发了用于医学图像分析的各种流形学习算法（Tran等，2011； Tran等，2013）。本文介绍了在脑肿瘤进展预测中应用增量流形学习方案（Tran。等，2013）与深度学习模型（Hinton等，2006）的比较研究。增量流形学习是流形学习算法的一种变体，用于处理大规模数据集，在该数据集中首先采样原始数据的代表性子集以构建流形骨架，然后通过遵循其局部几何形状将其余数据点插入到骨架中。增量流形学习算法旨在减轻与大规模数据集的传统流形学习方法相关的计算负担。深度学习是最近开发的多层感知器模型，已在许多应用程序中实现了最先进的性能。最近的一项名为“ Dropout”的技术可以通过防止体重共适应以避免过度拟合来进一步增强深度模型（Hinton等人，2012）。我们将这两种模型应用于来自四名脑肿瘤患者的多次MRI扫描中，以预测肿瘤的进展，并比较了两种模型在平均预测准确性，敏感性，特异性和准确性方面的表现。定量表现指标是四位患者的平均值。实验结果表明，与使用原始数据和主成分分析（PCA）相比，流形学习和深度神经网络模型均产生了更好的结果，并且深度学习模型比对该数据集进行流形学习是一种更好的方法。深度学习的平均敏感性和特异性可与多种学习方法相媲美，而其精确度则更高。这意味着通过深度学习预测的异常点更有可能对应于实际的进展区域。

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  • 来源
    《Image processing: algorithms and systems XIII》|2015年|93990W.1-93990W.7|共7页
  • 会议地点 San Francisco CA(US)
  • 作者

    Deqi Zhou; Loc Tran; Jihong Wang; Jiang Li;

  • 作者单位

    The IB Program Princess Anne High School 4400 Virginia Beach Boulevard, Virginia Beach, VA 23462;

    Department of Electric and Computer Engineering, Old Dominion University, Norfolk, VA 23508;

    Department of Imaging Physics, the University of Texas MD Anderson Cancer Center, Houston, TX 77030;

    Department of Electric and Computer Engineering, Old Dominion University, Norfolk, VA 23508,Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology, Guilin, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Magnetic resonance imaging; Manifold learning; Deep learning; Brain tumor diagnosis;

    机译：磁共振成像；流形学习；深度学习；脑肿瘤诊断;

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