用SAM自动生成医学图像伪标签，实现弱监督分割，省时省力。
Medical Image Segmentation with SAM-generated Annotations
- 用SAM的框提示生成伪标签，替代人工标注
- 弱监督训练的UNet模型性能接近全监督版本
- 适合缺乏标注数据的医学图像分割研究
医学图像分割受限于大规模公开标注数据集的缺乏。由于隐私原因，并非所有数据集都公开，且大规模标注需专业人员耗时费力。为此，我们评估了分割一切模型（SAM）作为医学数据标注工具的性能，利用其在医学分割十项全能（MSD）CT任务上生成“伪标签”。这些伪标签被用于弱监督训练UNet模型。我们测试了不同提示类型，发现框提示是生成伪标签的简单有效方法。该方法使模型性能达到与全监督模型相当水平。
原文摘要 · Abstract (English)
The field of medical image segmentation is hindered by the scarcity of large, publicly available annotated datasets. Not all datasets are made public for privacy reasons, and creating annotations for a large dataset is time-consuming and expensive, as it requires specialized expertise to accurately identify regions of interest (ROIs) within the images. To address these challenges, we evaluate the performance of the Segment Anything Model (SAM) as an annotation tool for medical data by using it to produce so-called "pseudo labels" on the Medical Segmentation Decathlon (MSD) computed tomography (CT) tasks. The pseudo labels are then used in place of ground truth labels to train a UNet model in a weakly-supervised manner. We experiment with different prompt types on SAM and find that the bounding box prompt is a simple yet effective method for generating pseudo labels. This method allows us to develop a weakly-supervised model that performs comparably to a fully supervised model.
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