仅用边界框标注实现医学图像分割，自动生成提示提高效率
Prompt learning with bounding box constraints for medical image segmentation
- 基于边界框自动构造提示，替代人工标注
- 在小数据下达到84.90%平均Dice分数
- 适合标注资源有限的医学影像研究者
医学图像像素级标注耗时且成本高。为减轻负担，基于易获取的边界框标注的弱监督方法成为实用替代方案。视觉基础模型在点或边界框提示下已展现出优异分割性能。提示学习通过适配这些模型并自动化分割流程，减少人工干预。然而现有方法依赖完整标注掩码。本文提出新框架，结合基础模型表征能力与弱监督标注效率，仅使用边界框即可自动生成提示。优化策略融合边界框导出的多重约束与模型生成的伪标签。多模态数据集上大量实验表明，该弱监督方法在有限数据下平均Dice达84.90%，优于现有全监督与弱监督方法。代码已开源。
原文摘要 · Abstract (English)
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
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