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首页> 外文期刊>IEEE Transactions on Medical Imaging >Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images
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Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

机译：从时空签名在光学相干断层扫描图像中预测黄斑水肿复发。

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摘要

Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients' response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and random forests-based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography (OCT) imaging data and predict individual patient outcome using these quantitative characteristics. We demonstrate on a data set of monthly SD-OCT scans of 155 patients with central retinal vein occlusion (CRVO) and 92 patients with branch retinal vein occlusion (BRVO) followed over one year that we can predict from initial three observations if the treated disease will recur within the covered interval. ET predicts the outcome on fivefold cross-validation with an area under the receiver operating characteristic curve (AuC) of 0.83 for BRVO and 0.76 for CRVO. Logistic regression achieved an AuC of 0.78 and 0.79, respectively. At the same time, the methods identified stable predictive signatures in the longitudinal imaging data that are the basis for accurate prediction. Furthermore, our results show that taking spatio-temporal features into account improves accuracy compared with features extracted at a single time-point. Our results demonstrate the feasibility of mining longitudinal data for predictive signatures, and building predictive models based on observed data.
机译：根据可用数据预测治疗反应是优化个性化治疗的关键。视网膜疾病需要长期治疗，患者的反应模式会有很大差异，从完全反应到疾病复发，以及需要以不同的间隔进行重新治疗。将高维观测中的可观察变量与结果联系起来具有挑战性。在本文中，我们介绍并评估在高维特征空间中运行的两种不同的数据驱动的机器学习方法：稀疏逻辑回归和基于随机森林的额外树（ET）。两者都基于在纵向光谱域光学相干断层扫描（OCT）成像数据中测量的视网膜厚度特征来识别时空特征，并使用这些定量特征预测患者的个体结局。我们通过对155名视网膜中央静脉阻塞（CRVO）和92例视网膜分支静脉阻塞（BRVO）的患者进行每月SD-OCT扫描的数据集进行了随访，在一年的时间里，我们可以从最初的三个观察结果中预测是否可以治疗该疾病将在涵盖的间隔内重复出现。 ET预测五重交叉验证的结果，BRVO的接收器工作特性曲线（AuC）下的面积为0.83，CRVO为0.76。 Logistic回归的AuC分别为0.78和0.79。同时，这些方法在纵向成像数据中确定了稳定的预测特征，这些特征是进行精确预测的基础。此外，我们的结果表明，与在单个时间点提取的特征相比，考虑时空特征可以提高准确性。我们的结果证明了挖掘纵向数据以获取预测特征以及基于观测数据建立预测模型的可行性。

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  • 来源
    《IEEE Transactions on Medical Imaging》 |2017年第9期|1773-1783|共11页
  • 作者

    Wolf-Dieter Vogl; Sebastian M. Waldstein; Bianca S. Gerendas; Ursula Schmidt-Erfurth; Georg Langs;

  • 作者单位

    Department of Biomedical Imaging and Imageguided Therapy, Computational Imaging Research Laboratory, Medical University Vienna, Vienna, Austria;

    Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Medical University Vienna, Vienna, Austria;

    Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Medical University Vienna, Vienna, Austria;

    Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Medical University Vienna, Vienna, Austria;

    Department of Biomedical Imaging and Imageguided Therapy, Computational Imaging Research Laboratory, Medical University Vienna, Vienna, Austria;

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  • 正文语种 eng
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  • 关键词

    Retina; Diseases; Feature extraction; Image segmentation; Imaging; Veins; Predictive models;

    机译：视网膜;疾病;特征提取;图像分割;成像;静脉;预测模型;

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