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.