  Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.
