Fig. 4
From: AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis
Automated flow path analysis and efficiency gains in different CMC designs. (A) Quantitative flow analysis of a CMC with six parallel channels: (a) results for br0 and (b) results for br1, comparing system-generated and user-tracked data. (B) Flow tracking in a CMC with splitting and merging regions, showing system- and user-generated results across four branches. (C) Flow tracking in a tree-shaped CMC featuring ten merging points and fifteen splitting points, representing a real-life CMC with a complex channel network: (a) extracted datapoints and (b) a photograph of the CMC highlighting critical points in red circles and branch numbers in green. (D) Comparison of fluid front tracking accuracy between AI-CMCA-generated and manually selected user-annotated center points for both (a) simple straight-channel CMC and (b) more complex split-merge-serpentine CMC. (E) Total time required for analysis across different CMC designs such as (a) straight channels, (b) split/merge, (c) serpentine channels, (d) split merge serpentine, SARS-Cov-2-POC testing15and GFAP POC testing16highlighting the efficiency gains of the automated system over manually tracking.
