结合频域增强与几何向量场的高密度液滴和微孔阵列芯片图像分割算法OA

Sample discretization is the core principle of single-molecule absolute quantification technologies based on droplets and micro-well array chips,such as digital polymerase chain reaction(dPCR).Consequently,high-precision image segmentation and counting of microreactors are critical prerequisites for achieving accurate concentration measurements.However,existing image processing algorithms still face dual challenges in precision and versatility,particularly under conditions of high-density microreactors,complex fluorescent backgrounds,and noise interference,where accurate segmentation remains elusive.To address these technical bottlenecks,this study proposes a U-Net model integrating frequency-domain enhancement and geometric vector fields(GVF).By mapping images to the frequency domain via Fourier transform,high-pass filters is utilized to suppress background noise while simultaneously strengthening the contour features of the microreactors.Furthermore,a geometric vector field mechanism is innovatively introduced that predicts the gradient flow field of pixels pointing toward the centroid of each microreactor.This approach enables instance-level precision in separating adhered microreactors,effectively resolving the core failure of existing algorithms under high-density conditions.Experimental results demonstrate that the proposed model achieves an average segmentation precision exceeding 99.9%across various scenarios,including droplet-based and microwell-based array chips,multiple fluorescence channels,and noisy environments.Compared with the industry-leading commercial software Crystal Miner,this model significantly improves the segmentation recall rate for high-density microreactors from below 90%to above 99.9%.This method exhibits exceptional generalization capabilities and robustness,effectively overcoming the challenges of adhesion and noise interference in high-density microreactor imaging.By breaking through the precision bottlenecks of current ultra-high-throughput sample processing,this research provides powerful technical support for high-reliability absolute quantitative detection in complex biological samples.