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.

翻译：近年来，多项无监督细胞分割方法被提出，旨在省去训练细胞分割模型时所需的繁重像素级标注工作。现有方法（几乎全部）通过聚焦不同细胞实例的检测来处理实例分割任务，却忽略了细胞类型识别。尽管此类模型在细胞计数等特定任务中表现足够有效，但其他应用场景需识别每个细胞的类型。本文提出CellMixer——一种创新的无标注异质细胞群体语义分割方法。本方法基于数据增强，能够利用同质细胞群体的图像级标签训练分割模型。实验结果表明，CellMixer可在多种细胞类型与成像模态下实现有竞争力的分割性能，验证了该方法在医学影像、细胞生物学及诊断学等领域更广泛应用的潜力与可扩展性。