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Communication Dans Un Congrès Année : 2023

Linking and analyzing correlative datasets

Résumé

Correlative microscopies is a set of methods allowing to fuse the information from complementary imaging modalities, usually also at different scales, on the same sample. In addition to the experimental elements helping to bridge different imaging modalities acquisitions, automated software solutions to correlate multiscale, multimodal and volumetric image data are an essential pillar of correlative microscopies workflows. They include in particular segmentation (identifying and spatially localizing objects in images), registration (aligning different images of the same or similar objects) and visualization (generating a comprehensive, potentially interactive, representation of the acquired imaging data), but also quantification of the accuracy and confidence in matching to create multidimensional measurements on the same biological object. Combining volume image together allow adding other information about the same object identified in both modalities, by fusing intensities with different meaning (different probes, or specific functional measurements) according to the modality used, or adding accurate shape information or neighborhood information only accessible from one of the modalities used. In this talk I will present the challenges of correlating 3D multimodal microscopies and of new methods to compute automatically the spatial relationship between sets of images or volumes of the same sample, but also to get the scientific information that is looked for. In particular, different ways of investigating and analyzing merged contents after fusion are relying on the way to quantitatively assess the accuracy of this fusion, and to take into account any artefact of registration, including interpolation, when merging the information itself at the object level.
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Dates et versions

hal-04217201 , version 1 (25-09-2023)

Identifiants

  • HAL Id : hal-04217201 , version 1

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Perrine Paul-Gilloteaux. Linking and analyzing correlative datasets. ALL-HANDS NODES MEETINg&MORE, iNext DISCOVERY, Apr 2023, Heidelberg, Germany. ⟨hal-04217201⟩
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