超越医学图像分割的像素级监督：从传统模型到基础模型
Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models
AI总结：
本文全面调研了基础模型时代前后利用弱标注进行医学图像分割的标注高效学习方法，并讨论了现有挑战，为基础模型推动该领域发展提供指导。
摘要：
医学图像分割在许多图像引导的临床方法中发挥着重要作用。然而，现有的分割算法大多依赖于带有像素级标注的完全标注图像进行训练，这既费时费力又需要专业知识，尤其是在只有专家才能提供可靠准确标注的医学成像领域。为了缓解这一挑战，人们越来越关注开发能够使用弱标注（如图像级、边界框、涂鸦和点）训练深度模型的分割方法。视觉基础模型的出现，尤其是Segment Anything Model (SAM)，通过大规模预训练实现了提示分割，为使用弱标注的分割任务引入了创新功能。采用基础模型结合传统学习方法在近期研究界引起了越来越多的兴趣，并展现出在现实世界应用的潜力。在本文中，我们全面调研了在基础模型时代之前及期间利用弱标注进行医学图像分割的标注高效学习的最新进展。此外，我们分析并讨论了现有方法的若干挑战，我们相信这将为塑造基础模型的发展轨迹以进一步推动医学图像分割领域提供宝贵指导。
英文摘要：
Medical image segmentation plays an important role in many image-guided clinical approaches. However, existing segmentation algorithms mostly rely on the availability of fully annotated images with pixel-wise annotations for training, which can be both labor-intensive and expertise-demanding, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. To alleviate this challenge, there has been a growing focus on developing segmentation methods that can train deep models with weak annotations, such as image-level, bounding boxes, scribbles, and points. The emergence of vision foundation models, notably the Segment Anything Model (SAM), has introduced innovative capabilities for segmentation tasks using weak annotations for promptable segmentation enabled by large-scale pre-training. Adopting foundation models together with traditional learning methods has increasingly gained recent interest research community and shown potential for real-world applications. In this paper, we present a comprehensive survey of recent progress on annotation-efficient learning for medical image segmentation utilizing weak annotations before and in the era of foundation models. Furthermore, we analyze and discuss several challenges of existing approaches, which we believe will provide valuable guidance for shaping the trajectory of foundational models to further advance the field of medical image segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
