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领域的关键研究方向，旨在通过引入人类反馈提升高成本标注的效率。该反馈形式包括点击、涂鸦或掩码，能够迭代优化模型输出，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推向新高度，仅医学成像领域就涌现出121种方法，推动该领域快速发展。本综述对该新兴领域进行了结构化梳理，涵盖全面的分类体系、现有方法的系统性评述以及对当前实践方法的深度分析。基于上述工作，我们探讨了该领域面临的挑战与机遇。例如，我们发现当前存在严重的方法间比较缺失问题，亟待通过标准化基准测试和评估基准加以解决。