Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model’s interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

基于人机循环机器学习的交互式医学图像分割是一种利用人类专家知识来辅助医学图像分割的新颖范式。然而，现有的方法常常陷入我们所说的交互误解，其本质是在短期和长期交互信息之间进行权衡的困境。为了更好地利用不同时间尺度的交互信息，我们提出了一种交互式分割框架，称为具有自适应置信校准的交互式医学图像分割（MECCA），它结合了基于动作的置信学习和多智能体强化学习。通过预测动作与短期交互信息的对齐水平来学习新颖的置信网络。然后提出了一种基于置信度的奖励塑造机制，将置信度明确地纳入策略梯度计算中，从而直接纠正模型的交互误解。MECCA 还分别通过标签生成和交互指导降低交互强度和难度，从而实现用户友好的交互。不同分割任务的数值实验表明，MECCA 可以在标记样本明显减少的情况下显着提高短期和长期交互信息利用效率。演示视频可从 https://bit.ly/mecca-demo-video 获取。MECCA 还分别通过标签生成和交互指导降低交互强度和难度，从而实现用户友好的交互。不同分割任务的数值实验表明，MECCA 可以在标记样本明显减少的情况下显着提高短期和长期交互信息利用效率。演示视频可从 https://bit.ly/mecca-demo-video 获取。MECCA 还分别通过标签生成和交互指导降低交互强度和难度，从而实现用户友好的交互。不同分割任务的数值实验表明，MECCA 可以在标记样本明显减少的情况下显着提高短期和长期交互信息利用效率。演示视频可从 https://bit.ly/mecca-demo-video 获取。