无需标注数据，直接从显微图像中分割细胞并生成表型特征。
Unsupervised Learning of Cell Instances with Generative Routing Pyramids
- 用粗到细的路由金字塔将像素关联到稀疏潜在源，实现无监督分割。
- 在多种细胞形态和成像条件下表现媲美有监督方法。
- 适合做细胞表型生成与无标注分析，科研人员可快速复现。
识别和表示细胞或细胞核等物体实例是显微图像分析中的常见任务。传统机器学习流程通常采用有监督检测或分割，再进行特征提取或分类，需人工标注，且将实例分割与细胞表征视为独立阶段。本文提出一种新的无监督方法，可从无标注显微图像中同时完成细胞实例分割与表型分类。该方法基于粗到细的路由金字塔重构图像，将像素关联至空间稀疏的潜在源。像素与潜在源的对应关系生成实例掩码，而潜在源编码细胞形态。我们在多种细胞形态和成像模态下验证了该方法在实例分割上的竞争力，并展示了在扰动条件下的细胞表型生成能力。代码与模型检查点见https://github.com/weigertlab/routing-pyramids。
原文摘要 · Abstract (English)
Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources. The resulting pixel-to-latent associations yield instance masks, while the source latents encode cell morphology. We demonstrate competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, as well as generative modeling of cellular phenotypes under perturbations. Source code and checkpoints are available at https://github.com/weigertlab/routing-pyramids.
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