In recent years, several unsupervised cell segmentation methods have been
presented, trying to omit the requirement of laborious pixel-level annotations
for the training of a cell segmentation model. Most if not all of these methods
handle the instance segmentation task by focusing on the detection of different
cell instances ignoring their type. While such models prove adequate for
certain tasks, like cell counting, other applications require the
identification of each cell's type. In this paper, we present CellMixer, an
innovative annotation-free approach for the semantic segmentation of
heterogeneous cell populations. Our augmentation-based method enables the
training of a segmentation model from image-level labels of homogeneous cell
populations. Our results show that CellMixer can achieve competitive
segmentation performance across multiple cell types and imaging modalities,
demonstrating the method's scalability and potential for broader applications
in medical imaging, cellular biology, and diagnostics.

近年来，已经提出了几种无监督的细胞分割方法，试图省略对细胞分割模型训练的繁琐像素级注释的要求。本文介绍了一种名为 CellMixer 的创新无标注方法，用于异质细胞群体的语义分割。我们基于数据增强的方法可以通过图像级标签的训练来构建分割模型，我们的结果表明 CellMixer 在多种细胞类型和成像模态下可以实现竞争性的分割性能，展示了该方法在医学成像、细胞生物学和诊断学等领域的可扩展性和潜力。