Survey on deep learning for radiotherapy
Survey on deep learning for radiotherapy
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DOI：
10.1016/j.compbiomed.2018.05.018
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发表时间：
2018-07-01
影响因子：
7.7
通讯作者：
Lallement, Alex
中科院分区：
文献类型：
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作者：
Meyer, Philippe;Noblet, Vincent;Lallement, Alex
超过50%的癌症患者接受放射治疗，无论是单独治疗还是与其他方法联合治疗。放射治疗的计划和实施是一个复杂的过程，但现在可以通过人工智能技术大大促进。深度学习是人工智能中发展最快的领域，近年来已成功应用于包括医学在内的许多领域。在本文中，我们首先解释了深度学习的概念，并在更广泛的机器学习背景下解决了它。介绍了最常见的网络架构，更具体地关注卷积神经网络。然后，我们对可应用于放射治疗的深度学习方法的已发表作品进行了综述，这些方法分为与患者工作流程相关的七个类别，并可以提供未来潜在应用的一些见解。我们试图让放射治疗和深度学习社区都能访问这篇论文，并希望它能激发这两个社区之间的新合作，以开发专门的放射治疗应用程序。
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine.In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.