Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

翻译：交互式分割是医学图像分析中的关键研究领域，旨在通过纳入人类反馈提升高成本标注的效率。这种反馈表现为点击、涂鸦或掩膜的形式，可实现模型输出的迭代优化，从而高效引导系统达成预期行为。近年来，基于深度学习的方法将研究结果推升至新高度，促使该领域快速发展，仅医学影像领域就涌现出121种方法。本综述为该新兴领域提供结构化概览，涵盖全面的分类体系、现有方法的系统梳理及当前实践的深度分析。基于上述贡献，我们探讨了该领域的挑战与机遇。例如，研究发现当前缺乏跨方法的系统性比较，亟需通过标准化基线与基准测试予以解决。