无需人工标注，自动训练模型分割重叠细胞核。
Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images
- 基于弱到强泛化，自动生成伪标签并扩展覆盖范围。
- 在多光谱全切片图像上实现新设备/协议下的零样本分割。
- 适合大规模病理图像分析，支持自动化质量自检。
我们提出一种弱到强泛化方法，用于全自动训练多头Mask-RCNN模型，结合高效通道注意力机制，在多路循环免疫荧光（multiplex cyclic IF）全切片图像（WSI）上可靠分割重叠细胞核。该方法通过伪标签修正和覆盖范围扩展，实现从新设备或新成像协议的全新图像中无监督学习新类别分割能力。我们还构建了生产环境中自动化的分割质量自诊断指标，避免人工逐图校验。该方法在五种主流方法上均表现显著提升。代码、示例WSI及高分辨率分割结果已开源，供社区使用与改进。
原文摘要 · Abstract (English)
We present a weak to strong generalization methodology for fully automated training of a multi-head extension of the Mask-RCNN method with efficient channel attention for reliable segmentation of overlapping cell nuclei in multiplex cyclic immunofluorescent (IF) whole-slide images (WSI), and present evidence for pseudo-label correction and coverage expansion, the key phenomena underlying weak to strong generalization. This method can learn to segment de novo a new class of images from a new instrument and/or a new imaging protocol without the need for human annotations. We also present metrics for automated self-diagnosis of segmentation quality in production environments, where human visual proofreading of massive WSI images is unaffordable. Our method was benchmarked against five current widely used methods and showed a significant improvement. The code, sample WSI images, and high-resolution segmentation results are provided in open form for community adoption and adaptation.
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