# Ultrafast Focus Detection for Automated Microscopy

arXiv: https://arxiv.org/abs/2108.12050 · HTML: https://mlsanity.com/paper/2108.12050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [Single-shot autofocusing of microscopy images using deep learning](https://mlsanity.com/paper/2003.09585/) (2020)
- [Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval](https://mlsanity.com/paper/2105.13967/) (2021)
- [Autofocusing technologies for whole slide imaging and automated microscopy](https://mlsanity.com/paper/2006.08610/) (2020)
- [Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis](https://mlsanity.com/paper/2010.08486/) (2020)
- [Flame Stability Analysis of Flame Spray Pyrolysis by Artificial Intelligence](https://mlsanity.com/paper/2011.08673/) (2020)

## Built on by (most cited first, 1 of 1)

- [Globus Automation Services: Research process automation across the space-time continuum](https://mlsanity.com/paper/2208.09513/) (2022)

## Abstract (arXiv)

Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus. We present a fast out-of-focus detection algorithm for electron microscopy images collected serially and demonstrate that it can be used to provide near-real-time quality control for neuroscience workflows. Our technique, textitMulti-scale Histologic Feature Detection, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. We exploit the inherent parallelism in the technique to employ GPU primitives in order to accelerate characterization. We show that our method can detect of out-of-focus conditions within just 20ms. To make these capabilities generally available, we deploy our feature detector as an on-demand service and show that it can be used to determine the degree of focus in approximately 230ms, enabling near-real-time use.