  Living cell segmentation from bright-field light microscopy 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 microscopy image segmentation tasks due to their success and
promising outcomes. The main objective of this paper is to develop a deep
learning, U-Net-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, a residual attention U-Net was proposed and
compared 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. The most accurate semantic segmentation results was
achieved 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.
