# A Mask R-CNN approach to counting bacterial colony forming units in pharmaceutical development

arXiv: https://arxiv.org/abs/2103.05337 · HTML: https://mlsanity.com/paper/2103.05337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 9 Mar 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep Learning to Detect Bacterial Colonies for the Production of Vaccines](https://mlsanity.com/paper/2009.00926/) (2020)

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

- [Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images](https://mlsanity.com/paper/2308.09436/) (2023)

## Abstract (arXiv)

We present an application of the well-known Mask R-CNN approach to the counting of different types of bacterial colony forming units that were cultured in Petri dishes. Our model was made available to lab technicians in a modern SPA (Single-Page Application). Users can upload images of dishes, after which the Mask R-CNN model that was trained and tuned specifically for this task detects the number of BVG- and BVG+ colonies and displays these in an interactive interface for the user to verify. Users can then check the model's predictions, correct them if deemed necessary, and finally validate them. Our adapted Mask R-CNN model achieves a mean average precision (mAP) of 94% at an intersection-over-union (IoU) threshold of 50%. With these encouraging results, we see opportunities to bring the benefits of improved accuracy and time saved to related problems, such as generalising to other bacteria types and viral foci counting.