# Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles

arXiv: https://arxiv.org/abs/2310.19630 · HTML: https://mlsanity.com/paper/2310.19630/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial](https://mlsanity.com/paper/2008.13690/) (2020)
- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI](https://mlsanity.com/paper/2012.05661/) (2020)

## Built on by (most cited first, 3 of 3)

- [Beyond Nearest Neighbor Interpolation in Data Augmentation](https://mlsanity.com/paper/2504.01527/) (2025)
- [Toward Optimal Adenovirus Detection Using YOLO26](https://mlsanity.com/paper/2607.17799/) (2026)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

Regular monitoring of the primary particles and purity profiles of a drug product during development and manufacturing processes is essential for manufacturers to avoid product variability and contamination. Transmission electron microscopy (TEM) imaging helps manufacturers predict how changes affect particle characteristics and purity for virus-based gene therapy vector products and intermediates. Since intact particles can characterize efficacious products, it is beneficial to automate the detection of intact adenovirus against a non-intact-viral background mixed with debris, broken, and artefact particles. In the presence of such particles, detecting intact adenoviruses becomes more challenging. To overcome the challenge, due to such a presence, we developed a software tool for semi-automatic annotation and segmentation of adenoviruses and a software tool for automatic segmentation and detection of intact adenoviruses in TEM imaging systems. The developed semi-automatic tool exploited conventional image analysis techniques while the automatic tool was built based on convolutional neural networks and image analysis techniques. Our quantitative and qualitative evaluations showed outstanding true positive detection rates compared to false positive and negative rates where adenoviruses were nicely detected without mistaking them for real debris, broken adenoviruses, and/or staining artefacts.