English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Particle-based Segmentation of Extended Objects on Curved Biological Membranes.

MPS-Authors
/cone/persons/resource/persons219678

Solomatina,  Anastasia
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219285

Kalaidzidis,  Yannis
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219059

Cezanne,  Alice
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219675

Soans,  Karen
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219494

Norden,  Caren
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219807

Zerial,  Marino
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219620

Sbalzarini,  Ivo F.
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Solomatina, A., Kalaidzidis, Y., Cezanne, A., Soans, K., Norden, C., Zerial, M., et al. (2021). Particle-based Segmentation of Extended Objects on Curved Biological Membranes. In ISBI 2021, IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1150-1154). Piscataway, N.J.: IEEE.


Cite as: https://hdl.handle.net/21.11116/0000-0008-DA5C-C
Abstract
We present a novel method for model-based segmentation of extended, blob-like objects on curved surfaces. Our method addresses several challenges arising when imaging curved biological membrane, such as out-of-membrane signal and geometry-induced background variations. We use a particle-based reconstruction of the membrane geometry, moment-conserving intensity interpolation from pixels to surface particles, and model-based in-surface segmentation. Our method denoises and deconvolves images, corrects for background variations, and quantifies the number, size, and intensity of segmented objects. We benchmark the accuracy of the method and present two applications to (1) neuroepithelial focal adhesion sites during optic cup morphogenesis in zebrafish and (2) reconstituted membrane domains bearing the small GTPase Rab5 on spherical beads.