一张医学影像可自动分割出多种用途的标签，且保持语义一致。
Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
- 基于新域图像自动生成多协议分割图，无需人工提示。
- 在7个未见数据集上显著优于现有模型，生成多个合理分割结果。
- 适合需要多角度分析医学影像的研究者使用。
同一张生物医学图像可根据不同应用需求进行多种有意义的分割，例如脑部MRI可按组织类型、血管区域、解剖区域、精细结构或病灶等进行分割。现有自动分割模型通常仅支持单一训练协议，或需大量人工提示来指定目标分割方式。我们提出Pancakes框架，针对来自新领域的未知图像，能自动生成多个合理协议的多标签分割图，并确保相关图像间语义一致。该框架提出了当前基础模型无法实现的新问题范式。在7个独立测试数据集上的实验表明，该模型在生成多个全图分割结果方面显著优于现有基础模型，且跨图像具有良好的语义连贯性。
原文摘要 · Abstract (English)
A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology, etc. Existing automatic segmentation models typically either (1) support only a single protocol, the one they were trained on, or (2) require labor-intensive manual prompting to specify the desired segmentation. We introduce Pancakes, a framework that, given a new image from a previously unseen domain, automatically generates multi-label segmentation maps for multiple plausible protocols, while maintaining semantic consistency across related images. Pancakes introduces a new problem formulation that is not currently attainable by existing foundation models. In a series of experiments on seven held-out datasets, we demonstrate that our model can significantly outperform existing foundation models in producing several plausible whole-image segmentations, that are semantically coherent across images.
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