Geometrical characterization of fluorescently labelled surfaces from noisy 3D microscopy data

Research output: Contribution to journalResearch articleContributedpeer-review


  • Elijah Shelton - , University of California at Santa Barbara (Author)
  • Friedhelm Serwane - , University of California at Santa Barbara, Max Planck Institute for Medical Research (Author)
  • Otger CampÀs - , University of California at Santa Barbara (Author)


Modern fluorescence microscopy enables fast 3D imaging of biological and inert systems alike. In many studies, it is important to detect the surface of objects and quantitatively characterize its local geometry, including its mean curvature. We present a fully automated algorithm to determine the location and curvatures of an object from 3D fluorescence images, such as those obtained using confocal or light-sheet microscopy. The algorithm aims at reconstructing surface labelled objects with spherical topology and mild deformations from the spherical geometry with high accuracy, rather than reconstructing arbitrarily deformed objects with lower fidelity. Using both synthetic data with known geometrical characteristics and experimental data of spherical objects, we characterize the algorithm's accuracy over the range of conditions and parameters typically encountered in 3D fluorescence imaging. We show that the algorithm can detect the location of the surface and obtain a map of local mean curvatures with relative errors typically below 2% and 20%, respectively, even in the presence of substantial levels of noise. Finally, we apply this algorithm to analyse the shape and curvature map of fluorescently labelled oil droplets embedded within multicellular aggregates and deformed by cellular forces.


Original languageEnglish
Pages (from-to)259-268
Number of pages10
JournalJournal of Microscopy
Issue number3
Publication statusPublished - Mar 2018
Externally publishedYes

External IDs

PubMed 28862753



  • 3D reconstruction, Confocal microscopy, differential surface analysis, image analysis, image segmentation, mean curvature, software