Medical Physics Division in the Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, 94305-5847, USA.
Institute for Computational and Mathematical Engineering, School of Engineering, Stanford University, Stanford, CA, 94305-4042, USA.
Med Phys. 2020 Jun;47(5):e148-e167. doi: 10.1002/mp.13649.
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep-learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep-learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.
近年来，在开发更准确、高效的医学和自然图像分割机器学习算法方面取得了重大进展。在这篇综述文章中，我们强调了机器学习算法在医学成像领域实现高效、准确分割的重要作用。我们特别关注了几篇关键的研究论文，这些论文涉及机器学习方法在生物医学图像分割中的应用。我们回顾了经典的机器学习算法，如马尔可夫随机场、k-均值聚类、随机森林等。虽然与深度学习技术相比，这些经典学习模型通常不够准确，但它们通常更具样本效率，并且结构更简单。我们还回顾了不同的深度学习架构，如人工神经网络（ANNs）、卷积神经网络（CNNs）和递归神经网络（RNNs），并展示了过去 3 年中发表的那些使用这些学习模型所获得的分割结果。我们强调了每种机器学习范例的成功和局限性。此外，我们还讨论了与不同机器学习模型训练相关的几个挑战，并提出了一些解决这些挑战的启发式方法。
