机器学习在癫痫诊断和预后中的应用
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
摘要：
机器学习在医学图像分析中发挥着越来越重要的作用，催生了神经影像学临床应用的新进展。关于机器学习与癫痫的研究，以往的研究主要集中在脑电（EEG）和立体脑电（SEEG）等电生理信号上，而忽视了神经影像学在癫痫研究中的潜力。神经影像学检查在确定癫痫灶范围方面具有重要优势，对术前评估和术后评估至关重要。然而，脑电图难以准确定位癫痫病灶在脑内的位置。在这篇综述中，我们强调了神经影像学和机器学习在癫痫诊断和预后方面的相互作用。我们首先概述了癫痫和癫痫临床中使用的典型神经影像学方式，MRI，DWI，fMRI和PET。然后，我们阐述了将机器学习方法应用于神经成像数据的两种方法：i）结合人工特征工程和分类器的传统机器学习方法，ii）深度学习方法，如卷积神经网络和自动编码器。随后，机器学习在癫痫神经成像中的应用，如分割，定位和定侧任务，以及与诊断和预后直接相关的任务进行了详细研究。最后，我们讨论了目前在这一领域的成就，挑战和潜在的未来方向，希望为癫痫的计算机辅助诊断和预后铺平道路。
英文摘要：
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: i) the conventional machine learning approach combining manual feature engineering and classifiers, ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.
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