CellStyle: 通过风格迁移改进零样本细胞分割
CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer
AI总结：
CellStyle通过风格迁移技术，在无需目标数据标注的情况下提升零样本细胞分割性能。
摘要：
细胞显微数据丰富；然而，相应的分割标注仍然稀缺。此外，细胞类型、成像设备和染色技术的差异在数据集之间引入了显著的领域差距。因此，即使在多样数据集（源数据集）上训练的大型预训练分割模型也难以泛化到未见数据集（目标数据集）。为了解决这一泛化问题，我们提出了CellStyle，该方法在不需要目标数据集标签的情况下提高此类模型的分割质量，从而实现零样本适应。CellStyle将未标注的目标数据集的属性（如纹理、颜色和噪声）转移到标注的源数据集。这种转移是在保持源图像细胞形状的同时进行的，确保现有的源标注仍然可以使用，同时保持目标数据集的视觉特征。带有现有标注的风格化合成图像使通用分割模型的微调成为可能，从而应用于未标注的目标数据。我们证明，通过在风格迁移数据上微调多个分割模型，CellStyle在多样数据集上显著提高了零样本细胞分割性能。代码将公开发布。
英文摘要：
Cell microscopy data are abundant; however, corresponding segmentation annotations remain scarce. Moreover, variations in cell types, imaging devices, and staining techniques introduce significant domain gaps between datasets. As a result, even large, pretrained segmentation models trained on diverse datasets (source datasets) struggle to generalize to unseen datasets (target datasets). To overcome this generalization problem, we propose CellStyle, which improves the segmentation quality of such models without requiring labels for the target dataset, thereby enabling zero-shot adaptation. CellStyle transfers the attributes of an unannotated target dataset, such as texture, color, and noise, to the annotated source dataset. This transfer is performed while preserving the cell shapes of the source images, ensuring that the existing source annotations can still be used while maintaining the visual characteristics of the target dataset. The styled synthetic images with the existing annotations enable the finetuning of a generalist segmentation model for application to the unannotated target data. We demonstrate that CellStyle significantly improves zero-shot cell segmentation performance across diverse datasets by finetuning multiple segmentation models on the style-transferred data. The code will be made publicly available.
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