Automated fluorescence image stitching for high-throughput and digital microfluidic biosensors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199472" target="_blank" >RIV/00216305:26220/26:0199472 - isvavai.cz</a>

Result on the web

<a href="https://pubs.rsc.org/en/content/articlelanding/2025/ra/d5ra08092d" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2025/ra/d5ra08092d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d5ra08092d" target="_blank" >10.1039/d5ra08092d</a>

Result language

angličtina

Original language name

Automated fluorescence image stitching for high-throughput and digital microfluidic biosensors

Original language description

Fluorescence imaging underpins digital PCR (dPCR), microarrays, and microfluidic biosensors, yet precise image integration remains a technical bottleneck when the sample area exceeds the microscope field of view. Current stitching methods often rely on fiducial markers or manual tuning, limiting automation and robustness, particularly in portable or point-of-care devices. We present a marker-free image stitching algorithm that combines partition-detection-based registration with mask-based illumination correction. The algorithm aligns frames using intrinsic structural features and compensates for brightness inconsistencies in an adaptive manner, without requiring platform-specific parameter tuning. Application to three dPCR systems, including droplet- and chip-based formats, showed an increased number of matched feature points within overlapping regions, improving the reliability of image stitching. In addition, it enhanced intensity uniformity by approximate to 29.6% compared with conventional methods. The proposed algorithm was further validated on microarrays and bead-based chips, demonstrating consistent stitching accuracy and signal integrity across different modalities. This generalized and automation-compatible solution supports high-throughput microfluidic imaging, quantitative bioanalysis, and integration with artificial intelligence-enabled diagnostic workflows.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10400 - Chemical sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

RSC Advances

ISSN

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e-ISSN

2046-2069

Volume of the periodical

15

Issue of the periodical within the volume

51

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

43436-43445

UT code for WoS article

001609427100001

EID of the result in the Scopus database

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