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.

中文翻译：

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用于生物医学图像分割的机器学习技术：技术方面概述和最先进应用介绍

近年来，在开发更准确、更高效的用于医学和自然图像分割的机器学习算法方面取得了重大进展。在这篇评论文章中，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。我们特别关注与机器学习方法在生物医学图像分割中的应用有关的几项关键研究。我们回顾了经典的机器学习算法，例如马尔可夫随机场、k均值聚类、随机森林等。尽管与深度学习技术相比，此类经典学习模型通常不太准确，但它们通常样本效率更高，复杂度也更低。结构。我们还回顾了不同的深度学习架构，例如人工神经网络（ANN）、卷积神经网络（CNN）和循环神经网络（RNN），并介绍了发表在过去3年 我们强调每个机器学习范例的成功和局限性。此外，我们讨论了与不同机器学习模型的训练相关的几个挑战，并提出了一些启发式方法来应对这些挑战。