  The ability to extrapolate gene expression dynamics in living single cells
requires robust cell segmentation, and one of the challenges is the amorphous
or irregularly shaped cell boundaries. To address this issue, we modified the
U-Net architecture to segment cells in fluorescence widefield microscopy images
and quantitatively evaluated its performance. We also proposed a novel loss
function approach that emphasizes the segmentation accuracy on cell boundaries
and encourages shape feature preservation. With a 97% sensitivity, 93%
specificity, 91% Jaccard similarity, and 95% Dice coefficient, our proposed
method called Residual Attention U-Net with edge-enhancement surpassed the
state-of-the-art U-Net in segmentation performance as evaluated by the
traditional metrics. More remarkably, the same proposed candidate also
performed the best in terms of the preservation of valuable shape features,
namely area, eccentricity, major axis length, solidity and orientation. These
improvements on shape feature preservation can serve as useful assets for
downstream cell tracking and quantification of changes in cell statistics or
features over time.
