Automatic thresholding method for high-throughput digital polymerase chain reaction fluorescence images with uneven illumination

The demand for nucleic acid detection is increasing worldwide as people are impacted by infectious diseases. Digital polymerase chain reaction (dPCR), which is a revolutionized nucleic acid absolute quantitative analysis technology, plays a significant role in the detection of genetic material. However, the traditional threshold-based methods fail to obtain high-accuracy segmentation results and real-time performance from dPCR fluorescence images with uneven illumination. Therefore, we proposed a robust two-stage automatic thresholding method to deal with dPCR fluorescence images. This method adopts the background subtraction method and the improved Otsu method to extract reaction chambers of dPCR images in two stages. Especially for the second stage, the peak-weaken method based on the improved Otsu method greatly improves the real-time segmentation of uneven illumination regions. Our method significantly improves the accuracy and processing speed of the dPCR image segmentation compared to several mainstream methods. This method could effectively segment the reaction chambers of dPCR images in complex environments. The average precision rate is 99.71%, the execution time is 0.139 s. Experimental results demonstrate that our proposed strategy has superior segmentation performance for high-throughput dPCR fluorescence images with uneven illumination. (C) 2022 SPIE and IS&T

Automated summary

A robust two-stage automatic thresholding method was proposed to deal with the segmentation of reaction chambers in dPCR fluorescence images. This method achieves high-accuracy segmentation results and improves real-time performance in the presence of uneven illumination.