DEMIS: Electron Microscopy Image Stitching using Deep Learning Features and Global Optimisation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F25%3APU155128" target="_blank" >RIV/00216305:26230/25:PU155128 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/publishedPapers/2025/133149/pdf/index.html" target="_blank" >https://www.scitepress.org/publishedPapers/2025/133149/pdf/index.html</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
DEMIS: Electron Microscopy Image Stitching using Deep Learning Features and Global Optimisation
Original language description
Accurate stitching of overlapping image tiles is essential for reconstructing large-scale Electron Microscopy (EM) images during Whole Slide Imaging. Current stitching approaches rely on handcrafted features and translation-only global alignment based on Minimum Spanning Tree (MST) construction. This results in suboptimal global alignment since it neglects rotational errors and works only with transformations estimated from pairwise feature matches, discarding valuable information tied to individual features. Moreover, handcrafted features may have trouble with repetitive textures. Motivated by the limitations of current methods and recent advancements in deep learning, we propose DEMIS, a novel EM image stitching method. DEMIS uses Local Feature TRansformer (LoFTR) for image matching, and optimises translational and rotational parameters directly at the level of individual features. For evaluation and training, we create EM424, a synthetic dataset generated by splitting high-resolution EM
Czech name
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Czech description
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Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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