Trypan Blue stained Cells Image Dataset

This Image Dataset contains 39 real world color images of trypan blue stained animal cells. Out of the 39 images, 21 were acquired using a digital camera mounted on an EclipseE200 (Nikon, Japan) bright field optical microscope with a 4x objective lens. For these images, NIH 3T3 cells were cultured, mixed on a 1:1 ratio with 0.4% trypan blue and loaded onto a Neubauer Chamber (Marienfeld, Germany). The field of view of each image is of approximately 2 mm × 1.5 mm, and its resolution of 2592×1936 pixels. The rest of the images were obtained from <b>Chan, L. L.-Y., Rice, W. L. &amp; Qiu, J. (2020). Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells. Plos one, 15(1), e0227950.</b> Real world images were used to create a larger Synthetic Image Dataset. In order to do so, single-cell masks were obtained from each image and classified according to its state: live or dead. Also, from each image, masks of cell clusters and debris were obtained and separated from its background. The synthetic image generation process is described as follows. First, a random background was selected from the background image pool and was resized to occupy a total of 3280x2464 pixels. Second, live and dead cell masks were randomly selected from the live and dead image pools and were pasted on top of the background using random <i>x</i> and <i>y</i> coordinates. Backgrounds, as well as live and dead masks, were randomly flipped (vertically) and/or mirrored (horizontally) and modified in its brightness, contrast and sharpness when generating each image. Cell masks were also rotated. Bounding box annotations were included in individual .txt files using the YOLO format and were automatically generated simultaneously with the image synthesis. A total of 2192 training images and 250 validation images were generated. Single-cell masks and backgrounds used for the image synthesis were obtained from 24 of the 39 real world images. The remaining 15 images (8, 16, 19, 21-25 &amp; 33-39) were used for model testing. The trained YOLOv4 cell counting model obtained a mAP₅₀of 87.30%, 88.47% of Precision and 90.24% of Recall in real world images. This model was used for the development of a stand-alone, portable and low-cost Automated cell-counter.

本图像数据集包含39张台盼蓝（trypan blue）染色动物细胞的真实场景彩色图像。在全部39张图像中，21张由搭载于日本尼康（Nikon）EclipseE200明场光学显微镜的数码相机采集，搭配4×物镜镜头。针对这批图像，实验人员先培养NIH 3T3细胞，以1:1的比例与0.4%台盼蓝混合，随后将混合液加载至德国Marienfeld公司生产的纽鲍尔计数板（Neubauer Chamber）中。每张图像的视场尺寸约为2 mm × 1.5 mm，分辨率为2592×1936像素。剩余图像均来自Chan、L.L.-Y.、Rice、W.L.与Qiu、J.于2020年发表于《PLOS ONE》的研究论文《观察与定量分析台盼蓝裂解死亡或濒死细胞的形态学效应》，该论文刊载于第15卷第1期，文章编号为e0227950。研究人员利用这批真实场景图像构建了规模更大的合成图像数据集。为此，研究人员从每张真实图像中提取单细胞掩码，并根据细胞状态将其分为活细胞与死细胞两类；此外还从每张图像中提取细胞团与细胞碎片的掩码，并将其与背景分离。合成图像的生成流程如下：首先，从背景图像库中随机选取背景图，并将其缩放至3280×2464像素尺寸；其次，从活细胞、死细胞掩码库中随机选取对应掩码，通过随机x、y坐标粘贴至背景图上。在生成每张图像时，背景图、活/死细胞掩码会被随机进行垂直翻转和/或水平镜像操作，并对其亮度、对比度与锐度进行调整；细胞掩码还会被随机旋转。研究人员采用YOLO格式将边界框标注信息存储于独立的.txt文件中，该标注与图像合成过程同步自动生成。最终共生成2192张训练图像与250张验证图像。用于图像合成的单细胞掩码与背景图均来自39张真实图像中的24张；剩余15张图像（编号8、16、19、21至25及33至39）被用于模型测试。经训练的YOLOv4细胞计数模型在真实场景图像上取得了87.30%的mAP₅₀、88.47%的精确率与90.24%的召回率。该模型被用于开发一款独立、便携且低成本的自动化细胞计数器。