Interactive segmentation reduces the annotation time of medical images and allows annotators to iteratively refine labels with corrective interactions, such as clicks. While existing interactive models transform clicks into user guidance signals, which are combined with images to form (image, guidance) pairs, the question of how to best represent the guidance has not been fully explored. To address this, we conduct a comparative study of existing guidance signals by training interactive models with different signals and parameter settings to identify crucial parameters for the model's design. Based on our findings, we design a guidance signal that retains the benefits of other signals while addressing their limitations. We propose an adaptive Gaussian heatmaps guidance signal that utilizes the geodesic distance transform to dynamically adapt the radius of each heatmap when encoding clicks. We conduct our study on the MSD Spleen and the AutoPET datasets to explore the segmentation of both anatomy (spleen) and pathology (tumor lesions). Our results show that choosing the guidance signal is crucial for interactive segmentation as we improve the performance by 14% Dice with our adaptive heatmaps on the challenging AutoPET dataset when compared to non-interactive models. This brings interactive models one step closer to deployment on clinical workflows. We will make our code publically available.

翻译：交互式分割可减少医学图像的标注时间，并允许标注者通过点击等矫正性交互迭代优化标签。现有交互模型将点击转化为用户引导信号，与图像组合形成（图像，引导）对，但如何最优表达引导信号的问题尚未充分探索。为此，我们通过训练具有不同引导信号和参数设置的交互模型，开展现有引导信号的比较研究，以识别模型设计的关键参数。基于研究发现，我们设计了一种兼具其他信号优势并弥补其局限性的引导信号：提出自适应高斯热图引导信号，该信号利用测地线距离变换在编码点击时动态调整每个热图的半径。我们在MSD脾脏数据集和AutoPET数据集上开展研究，探索解剖结构（脾脏）与病理区域（肿瘤病灶）的分割。结果表明，引导信号的选择对交互式分割至关重要——在具有挑战性的AutoPET数据集上，与无交互模型相比，我们的自适应热图将Dice指标提升了14%。这一进展使交互模型更接近临床工作流部署。我们将公开相关代码。