[A digital droplet PCR detection technique based on filter faster R-CNN].

ABSTRACT:

Objective

To propose a method for mitigate the impact of anomaly points (such as dust, bubbles, scratches on the chip surface, and minor indentations) in images on the results of digital droplet PCR (ddPCR) detection to achieve high-throughput, stable, and accurate detection.

Methods

We propose a Filter Faster R-CNN ddPCR detection model, which employs Faster R-CNN to generate droplet prediction boxes followed by removing the anomalies within the positive droplet prediction boxes using an outlier filtering module (Filter). Using a plasmid carrying a norovirus fragment as the template, we established a ddPCR dataset for model training (2462 instances, 78.56%) and testing (672 instances, 21.44%). Ablation experiments were performed to test the effectiveness of 3 filtering bran

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10954537 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature