Telecentric bright-field transmitted light microscopic dataset

Living cell segmentation from bright-field light microscopic images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopic image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, UNet-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy. To find the most suitable architecture for our datasets, we have proposed a residual attention U-Net and compared it with an attention and a simple U-Net architecture. The attention mechanism highlights the remarkable features and suppresses activations in the irrelevant image regions. The residual mechanism overcomes with vanishing gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention U-Net, respectively. We achieved the most accurate semantic segmentation results in the Mean-IoU and Dice metrics by applying the residual and attention mechanisms together. The watershed method applied to this best – Residual Attention – semantic segmentation result gave the segmentation with the specific information for each cell.Funding provided by: Ministerstvo Školství, Mládeže a TělovýchovyCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001823Award Number: CENAKVA (LM2018099)Funding provided by: European Regional Development FundCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100008530Award Number: ATCZ215Funding provided by: GAJU*Crossref Funder Registry ID: Award Number: 017/2016/ZHuman HeLa cell line was growing to low optical density overnight at 37◦ C, 5% CO2, and 90% RH. The nutrient solution consisted of DMEM (87.7%) with high glucose (>1 g L−1 ), fetal bovine serum (10%), antibiotics and antimycotics (1%), L-glutamine (1%), and gentamicin (0.3%; all purchased from Biowest, Nu aille, France). The HeLa cells were maintained in a Petri dish with a cover glass bottom and lid at room temperature of 37◦ C when we were running experiments during data collection phase in different time laps experiments. We captured time-lapse image series of living human HeLa cells using a high resolved bright-field light microscope for observation of sub-microscopic objects and cells. This microscope was designed by the Institute of Complex System (ICS, Nov´e Hrady, Czech Republic) and built by Optax (Prague, Czech Republic) and ImageCode (Brloh, Czech Republic) in 2021. The microscope has a simple construction of the optical path. The light from two light-emitting diods CL-41 (Optika Microscopes, Ponteranica, Italy) was passing through a sample to reach a telecentric measurement objective TO4.5/43.4-48-F-WN (Vision & Control GmbH, Shul, Germany) and an Arducam AR1820HS 1/2.3-inch 10-bit RGB camera with a chip of 4912×3684 pixel resolution. The images were captured as a primary (raw) signal with theoretical pixel size (size of the object projected onto the camera pixel) of 113 nm. The software (developed by the ICS) controls to capture the primary signal with the camera exposure of 2.75 ms. All the experiments we performed in time-lapse to observe cells' behaviour over time