标题： 带有边界框约束的医学图像分割提示学习 显示英文标题

标题： Prompt learning with bounding box constraints for medical image segmentation

摘要： 像素级标注在医学领域中以耗时且昂贵而著称。为了减轻这一负担，基于边界框标注的弱监督方法——这些标注更容易获取——提供了一个实用的替代方案。视觉基础模型在提供点或边界框等提示时，最近显示出显著的分割性能。提示学习通过将这些模型适应于下游任务并自动化分割来利用这些模型，从而减少用户干预。然而，现有的提示学习方法依赖于完全标注的分割掩码。本文提出了一种新框架，将基础模型的表示能力与弱监督分割的标注效率相结合。更具体地说，我们的方法仅使用边界框标注来自动化基础模型的提示生成。我们提出的优化方案将从边界框标注中得出的多个约束与由提示基础模型生成的伪标签相结合。在多模态数据集上的广泛实验表明，我们的弱监督方法在有限数据设置下平均Dice得分为84.90%，优于现有的全监督和弱监督方法。代码可在https://github.com/Minimel/box-prompt-learning-VFM.git获得。 显示英文摘要

摘要： Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised approaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multimodal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code is available at https://github.com/Minimel/box-prompt-learning-VFM.git