# Deep Learning-Based Semantic Segmentation of Microscale Objects

## TL;DR

This paper introduces a deep learning model for semantic segmentation of microscale objects in crowded environments, achieving high accuracy and improving automated manipulation techniques like optical tweezers.

## Contribution

The paper presents a novel deep learning approach that significantly enhances segmentation accuracy in complex microscale environments compared to traditional methods.

## Key findings

- Achieved a mean Intersection Over Union score of 0.91.
- Successfully segmented crowded microscale environments.
- Improved accuracy over traditional computer vision algorithms.

## Abstract

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.