用于细胞核分割的多模态显微镜图像风格迁移
Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation
AI总结：
本文提出基于GAN且解耦内容与风格的多模态显微镜图像增强方法，可跨细胞检测、光照和成像模态保留结构并转换风格，显著提升Mask R-CNN细胞核分割性能。
摘要：
为细胞核分割标注显微镜图像既费力又耗时。为了利用少量现有标注（包括跨多种模态的标注），我们提出了一种基于生成对抗网络（GAN）的新型显微镜风格增强技术。与其他风格迁移方法不同，该技术不仅能够处理不同的细胞检测类型和光照条件，还能处理明场和荧光显微镜等不同成像模态。通过对内容和风格使用解耦表示，我们可以在增强过程中改变图像风格，同时保留原始图像的结构。我们在包含多种细胞检测、光照条件和成像模态的2018 Data Science Bowl数据集上评估了该数据增强方法。借助我们的风格增强，竞赛中排名前两位的基于Mask R-CNN的细胞核分割算法的分割准确率显著提高。因此，我们的增强技术使下游任务对测试数据异质性更加鲁棒，并有助于在不对少数类进行重采样的情况下抵消类别不平衡。
英文摘要：
Annotating microscopy images for nuclei segmentation is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it can not only deal with different cell assay types and lighting conditions, but also with different imaging modalities, such as bright-field and fluorescence microscopy. Using disentangled representations for content and style, we can preserve the structure of the original image while altering its style during augmentation. We evaluate our data augmentation on the 2018 Data Science Bowl dataset consisting of various cell assays, lighting conditions, and imaging modalities. With our style augmentation, the segmentation accuracy of the two top-ranked Mask R-CNN-based nuclei segmentation algorithms in the competition increases significantly. Thus, our augmentation technique renders the downstream task more robust to the test data heterogeneity and helps counteract class imbalance without resampling of minority classes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
