Probabilistic multi-compartmenty geometric model: Application to cell segmentation

S. Farhand, Ramon Montero, X. Vial, D. T. Nguyen, M. Reardon, S. M. Pham, Fotios M Andreopoulos, G. Tsechpenakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We describe a cell segmentation approach based on a probabilistic formulation of multi-compartment level set-based deformable model. We aim at the simultaneous cell partitioning into nucleus and membrane. We consider relative topology of the two distinct cell compartments, while we constrain our solution using shape prior information. Our method integrates geometric models with learning-based classification in a simple graphical model, such that it captures not only the cell compartments but also their topological relationship. We apply our framework to (static) fluorescent microscopy images, where the cultured cells are stained with calcein AM.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Biomedical Imaging
Pages174-177
Number of pages4
DOIs
StatePublished - Aug 15 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Fingerprint

Microscopic examination
Cells
Topology
Membranes
Microscopy
Cultured Cells
Learning
calcein AM

Keywords

  • cell segmentation
  • fluorescent microscopy
  • multi-compartment geometric model

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Farhand, S., Montero, R., Vial, X., Nguyen, D. T., Reardon, M., Pham, S. M., ... Tsechpenakis, G. (2012). Probabilistic multi-compartmenty geometric model: Application to cell segmentation. In Proceedings - International Symposium on Biomedical Imaging (pp. 174-177). [6235512] https://doi.org/10.1109/ISBI.2012.6235512

Probabilistic multi-compartmenty geometric model : Application to cell segmentation. / Farhand, S.; Montero, Ramon; Vial, X.; Nguyen, D. T.; Reardon, M.; Pham, S. M.; Andreopoulos, Fotios M; Tsechpenakis, G.

Proceedings - International Symposium on Biomedical Imaging. 2012. p. 174-177 6235512.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Farhand, S, Montero, R, Vial, X, Nguyen, DT, Reardon, M, Pham, SM, Andreopoulos, FM & Tsechpenakis, G 2012, Probabilistic multi-compartmenty geometric model: Application to cell segmentation. in Proceedings - International Symposium on Biomedical Imaging., 6235512, pp. 174-177, 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, Barcelona, Spain, 5/2/12. https://doi.org/10.1109/ISBI.2012.6235512
Farhand S, Montero R, Vial X, Nguyen DT, Reardon M, Pham SM et al. Probabilistic multi-compartmenty geometric model: Application to cell segmentation. In Proceedings - International Symposium on Biomedical Imaging. 2012. p. 174-177. 6235512 https://doi.org/10.1109/ISBI.2012.6235512
Farhand, S. ; Montero, Ramon ; Vial, X. ; Nguyen, D. T. ; Reardon, M. ; Pham, S. M. ; Andreopoulos, Fotios M ; Tsechpenakis, G. / Probabilistic multi-compartmenty geometric model : Application to cell segmentation. Proceedings - International Symposium on Biomedical Imaging. 2012. pp. 174-177
@inproceedings{b3beb591ce6349f39b60187019d7afd2,
title = "Probabilistic multi-compartmenty geometric model: Application to cell segmentation",
abstract = "We describe a cell segmentation approach based on a probabilistic formulation of multi-compartment level set-based deformable model. We aim at the simultaneous cell partitioning into nucleus and membrane. We consider relative topology of the two distinct cell compartments, while we constrain our solution using shape prior information. Our method integrates geometric models with learning-based classification in a simple graphical model, such that it captures not only the cell compartments but also their topological relationship. We apply our framework to (static) fluorescent microscopy images, where the cultured cells are stained with calcein AM.",
keywords = "cell segmentation, fluorescent microscopy, multi-compartment geometric model",
author = "S. Farhand and Ramon Montero and X. Vial and Nguyen, {D. T.} and M. Reardon and Pham, {S. M.} and Andreopoulos, {Fotios M} and G. Tsechpenakis",
year = "2012",
month = "8",
day = "15",
doi = "10.1109/ISBI.2012.6235512",
language = "English",
isbn = "9781457718588",
pages = "174--177",
booktitle = "Proceedings - International Symposium on Biomedical Imaging",

}

TY - GEN

T1 - Probabilistic multi-compartmenty geometric model

T2 - Application to cell segmentation

AU - Farhand, S.

AU - Montero, Ramon

AU - Vial, X.

AU - Nguyen, D. T.

AU - Reardon, M.

AU - Pham, S. M.

AU - Andreopoulos, Fotios M

AU - Tsechpenakis, G.

PY - 2012/8/15

Y1 - 2012/8/15

N2 - We describe a cell segmentation approach based on a probabilistic formulation of multi-compartment level set-based deformable model. We aim at the simultaneous cell partitioning into nucleus and membrane. We consider relative topology of the two distinct cell compartments, while we constrain our solution using shape prior information. Our method integrates geometric models with learning-based classification in a simple graphical model, such that it captures not only the cell compartments but also their topological relationship. We apply our framework to (static) fluorescent microscopy images, where the cultured cells are stained with calcein AM.

AB - We describe a cell segmentation approach based on a probabilistic formulation of multi-compartment level set-based deformable model. We aim at the simultaneous cell partitioning into nucleus and membrane. We consider relative topology of the two distinct cell compartments, while we constrain our solution using shape prior information. Our method integrates geometric models with learning-based classification in a simple graphical model, such that it captures not only the cell compartments but also their topological relationship. We apply our framework to (static) fluorescent microscopy images, where the cultured cells are stained with calcein AM.

KW - cell segmentation

KW - fluorescent microscopy

KW - multi-compartment geometric model

UR - http://www.scopus.com/inward/record.url?scp=84864834771&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864834771&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2012.6235512

DO - 10.1109/ISBI.2012.6235512

M3 - Conference contribution

AN - SCOPUS:84864834771

SN - 9781457718588

SP - 174

EP - 177

BT - Proceedings - International Symposium on Biomedical Imaging

ER -