Model-Based and Data-Driven Strategies in Medical Image Computing
Model-Based and Data-Driven Strategies in Medical Image Computing
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DOI：
10.1109/jproc.2019.2943836
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发表时间：
2020-01-01
影响因子：
20.6
通讯作者：
Schnabel, Julia A.
中科院分区：
文献类型：
--
作者：
Rueckert, Daniel;Schnabel, Julia A.
基于模型的图像重建、分析和解释方法在过去几十年中取得了重大进展。这些方法中的许多都是基于数学、物理或生物模型。这些方法的一个挑战是对底层过程（例如，图像获取的物理学或疾病的病理生理学），具有适当的细节和真实性水平。随着大量成像数据和机器学习（特别是深度学习）技术的可用性，数据驱动方法已变得越来越广泛，用于重建，分析和解释的不同任务。这些方法直接从标记或未标记的图像数据中学习统计模型，并且已经被证明对于从医学成像中提取临床有用的信息非常强大。虽然这些数据驱动的方法通常优于传统的基于模型的方法，但它们的临床部署通常在鲁棒性、泛化能力和可解释性方面带来挑战。在本文中，我们讨论了哪些发展促使从基于模型的方法转向数据驱动的策略，以及与纯数据驱动方法（特别是深度学习）相关的潜在问题。我们还讨论了数据驱动方法面临的一些挑战，例如，对新的未见过数据的泛化（例如，迁移学习）、对抗攻击的鲁棒性和可解释性。最后，我们总结了这些方法如何可能导致更紧密耦合的成像管道，以端到端的方式进行优化的发展的讨论。
Model-based approaches for image reconstruction, analysis, and interpretation have made significant progress over the past decades. Many of these approaches are based on either mathematical, physical, or biological models. A challenge for these approaches is the modeling of the underlying processes (e.g., the physics of image acquisition or the patho-physiology of a disease) with appropriate levels of detail and realism. With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis, and interpretation. These approaches learn statistical models directly from labeled or unlabeled image data and have been shown to be very powerful for extracting clinically useful information from medical imaging. While these data-driven approaches often outperform traditional model-based approaches, their clinical deployment often poses challenges in terms of robustness, generalization ability, and interpretability. In this article, we discuss what developments have motivated the shift from model-based approaches toward data-driven strategies and what potential problems are associated with the move toward purely data-driven approaches, in particular deep learning. We also discuss some of the open challenges for data-driven approaches, e.g., generalization to new unseen data (e.g., transfer learning), robustness to adversarial attacks, and interpretability. Finally, we conclude with a discussion on how these approaches may lead to the development of more closely coupled imaging pipelines that are optimized in an end-to-end fashion.