# Deep Learning-Based Semantic Segmentation of Microscale Objects

Source: [Deep Learning-Based Semantic Segmentation of Microscale Objects](<https://www.sota2.com/research/paper/68fa65ff1e209131cea0ddb5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:41:29.853Z

Authors: Ekta U. Samani, Wei Guo, Ashis G. Banerjee

Published: 2019-07-03T23:07:01.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1907.03576>)

[PDF](<https://arxiv.org/pdf/1907.03576.pdf>)