Comet assay annotation data for comet segmentation and scoring (1037 comet assay images)

Comet assay annotation images used in the following paper Hong, Y., Han, HJ., Lee, H. et al. Deep learning method for comet segmentation and comet assay image analysis. Sci Rep 10, 18915 (2020). https://doi.org/10.1038/s41598-020-75592-7 DeepComet online service website: https://ad3.io/ sign up->dashboard->APPs->Deep Comets->new job Two biology researchers who were experienced in the comet assay for over half a year under the guidance of a toxicology expert in the assay annotated the images independently and compared each other’s work to ensure consistency. Here, the VGG Image Annotator sofware was used to annotate the images manually. We annotated the intact comet heads without a tail using a circular region tool that outputs the center and radius of the circle. Further, comets with a tail and DNA damage were annotated with a polyline tool, where the frst dot was marked at the center of a comet head, and the second dot was marked at the bottom of the comet head. Tese two points were used to identify the head of a comet. Subsequently, dots were marked along the boundary of the comet tail counterclockwise from the second dot. Te total number of dots were at least eight per each comet. Comets can be sorted into non-ghost cells with a distinct head and tail, and ghost cells with a small or no nucleoid head and a broad tail. The ghost cells are usually classifed as the highest degree of DNA damage by visual scoring. Until now, there is a controversy over the cause of ghost cells and how they should be analyzed appropriately. Therefore, our method is designed to classify these two kinds of cells, so one can use some custom analysis techniques for ghost cells. The comets in our datasets were all classifed as non-ghost or ghost cells. Furthermore, we assigned tags to the comets if they were overlapped or located at the boundary of an image (i.e., only a part of the comet is visible). After manual annotation with dots and tags (in the red box), each dotted contour was flled up to give mask images.本数据集为用于下述论文的彗星试验（comet assay）注释图像。 论文作者为Hong Y、Han HJ、Lee H等，其发表于《科学报告（Scientific Reports）》2020年第10卷第18915篇的《用于彗星分割与彗星试验图像分析的深度学习方法》，DOI：10.1038/s41598-020-75592-7。 DeepComet在线服务网址：https://ad3.io/，访问路径为：注册→仪表盘→应用→Deep Comets→新建任务。 两位具备半年以上彗星试验实操经验的生物学研究人员，在该领域毒理学专家的指导下独立完成图像注释，并通过交叉校核确保注释结果的一致性。本次注释采用VGG图像注释器（VGG Image Annotator）软件进行手动标注。对于无尾的完整彗星头，使用圆形区域工具标注，输出该圆形的圆心与半径信息。对于带有尾部且存在DNA损伤的彗星，则采用折线工具标注：首个标记点设于彗星头的中心位置，第二个标记点设于彗星头的底部，以此两点确定彗星头部的范围；随后从第二个标记点出发，沿彗星尾部的边界逆时针方向标记若干点，单颗彗星的标记点总数不少于8个。 彗星可分为两类：一类为具有清晰头尾结构的非鬼影细胞（non-ghost cells），另一类为具有小型或无类核头、尾部宽阔的鬼影细胞（ghost cells）。通过视觉评分时，通常将鬼影细胞判定为DNA损伤程度最高的类别。目前学界对于鬼影细胞的成因及恰当的分析方式仍存在争议。因此本方法旨在区分这两类细胞，以便用户可针对鬼影细胞采用自定义分析技术。 本数据集内的所有彗星均被标注为非鬼影细胞或鬼影细胞。此外，若彗星存在重叠情况，或位于图像边界（即仅可见部分躯体），则为其添加对应标签。在完成带标记点与标签（红框标注）的手动注释后，对每个带标记点的轮廓进行填充，以生成掩码图像（mask images）。