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 Chan, L. L.-Y., Rice, W. L. & Qiu, J. (2020). Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells. Plos one, 15(1), e0227950. 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 x and y 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 & 33-39) were used for model testing. The trained YOLOv4 cell counting model obtained a mAP50 of 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。其中21张图像通过搭载于尼康（Nikon，日本）EclipseE200正置明场光学显微镜的数码相机，搭配4×物镜采集获得。该组图像对应的样本制备流程为：将NIH 3T3细胞培养后，以1:1比例与0.4%台盼蓝混合，随后加载至Neubauer计数板（Marienfeld，德国）中。单张图像的视场范围约为2 mm × 1.5 mm，分辨率为2592×1936像素。
剩余图像取自Chan L L-Y、Rice W L与Qiu J于2020年发表在《PLOS ONE》第15卷第1期、文章编号为e0227950的论文《台盼蓝破裂死亡或濒死细胞的形态学效应观察与定量分析》（原文标题：Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells）。
研究团队利用上述真实场景图像构建了规模更大的合成图像数据集。具体实现步骤如下：首先从每张真实图像中提取单细胞掩码，并根据细胞状态划分为活细胞或死细胞两类；同时从每张图像中提取细胞团块与细胞碎片的掩码，并将其与背景分离。
合成图像的生成流程如下：第一步，从背景图像库中随机选取一张背景图，将其缩放至3280×2464像素；第二步，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坐标将其粘贴至背景图上。在生成单张图像时，会对背景、活细胞及死细胞掩码随机执行垂直翻转、水平镜像操作，并调整其亮度、对比度与锐度；同时对细胞掩码进行旋转操作。
目标检测框标注以YOLO格式存储于独立的.txt文件中，且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素材取自39张真实图像中的24张；剩余15张图像（编号为8、16、19、21-25及33-39）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的mAP50为87.30%，精确率为88.47%，召回率为90.24%。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仪。