OnePetri Image Dataset
In microbiology, a plaque is defined as a “clear area on an otherwise opaque field of bacteria that indicates the inhibition or dissolution of the bacterial cells by some agent, either a virus or an antibiotic. Plaques are a sensitive laboratory indicator of the presence of some anti-bacterial factor.”
When working with bacteriophages (phages), viruses which can only infect and kill bacteria, scientists often need to perform the time-intensive monotonous task of counting plaques on Petri dishes. To help solve this problem I developed OnePetri, a set of machine learning models and a mobile phone application (currently iOS-only) that accelerates common microbiological Petri dish assays using AI.
A task that once took microbiologists several minutes to do per Petri dish (adds up quickly considering there are often tens of Petri dishes to analyze at a time!) could now be mostly automated thanks to computer vision, and completed in a matter of seconds.
App in Action
A total of 43 source images were used in this dataset with the following split: 29 training, 9 validation, 5 testing (2505 images after preprocessing and augmentations are applied).
OnePetri is a mobile phone application (currently iOS-only) which accelerates common microbiological Petri dish assays using AI. OnePetri's YOLOv5s plaque detection model was trained on a diverse set of images from the HHMI's SEA-PHAGES program, many of which are included in this dataset. This project wouldn't be possible without their support!
The following pre-processing options were applied:
- Tile image into 5 rows x 5 columns
- Resize tiles to 416px x 416px
The following augmentation options were applied:
- Grayscale (35% of images)
- Hue shift (-45deg to +45deg)
- Blur up to 2px