  Accurate estimation of the positions and shapes of microscale objects is
crucial for automated imaging-guided manipulation using a non-contact technique
such as optical tweezers. Perception methods that use traditional computer
vision algorithms tend to fail when the manipulation environments are crowded.
In this paper, we present a deep learning model for semantic segmentation of
the images representing such environments. Our model successfully performs
segmentation with a high mean Intersection Over Union score of 0.91.
