  Interactive medical image segmentation refers to the accurate segmentation of
the target of interest through interaction (e.g., click) between the user and
the image. It has been widely studied in recent years as it is less dependent
on abundant annotated data and more flexible than fully automated segmentation.
However, current studies have not fully explored user-provided prompt
information (e.g., points), including the knowledge mined in one interaction,
and the relationship between multiple interactions. Thus, in this paper, we
introduce a novel framework equipped with prompt enhancement, called PE-MED,
for interactive medical image segmentation. First, we introduce a Self-Loop
strategy to generate warm initial segmentation results based on the first
prompt. It can prevent the highly unfavorable scenarios, such as encountering a
blank mask as the initial input after the first interaction. Second, we propose
a novel Prompt Attention Learning Module (PALM) to mine useful prompt
information in one interaction, enhancing the responsiveness of the network to
user clicks. Last, we build a Time Series Information Propagation (TSIP)
mechanism to extract the temporal relationships between multiple interactions
and increase the model stability. Comparative experiments with other
state-of-the-art (SOTA) medical image segmentation algorithms show that our
method exhibits better segmentation accuracy and stability.
