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 language | English |
|---|---|
| Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
| Pages | 174-177 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Aug 15 2012 |
| Event | 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: May 2 2012 → May 5 2012 |
Other
| Other | 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 5/2/12 → 5/5/12 |
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Keywords
- cell segmentation
- fluorescent microscopy
- multi-compartment geometric model
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
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 proceeding › Conference contribution
}
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 -