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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.

Automatic segmentation of lung lesions from COVID-19 Computed Tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this work provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: Firstly, the proposed Region of Interest (ROI) extraction implements patch mechanism strategy to satisfy the applicability of 3D network and remove irrelevant background. Secondly, 3D network is established to extract spatial features, where 3D Attention model promotes network to enhance target area. Then, to improve convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and Conditional Random Field (CRF) are applied to realize data resampling and binary segmentation. This method was assessed with some comparative Experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

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