Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters
This paper describes an automated visual tracking system combining time-lapse and end-point confocal microscopy to aid the interpretations of cell behaviors and interactions, with the focus on understanding the sprouting mechanism during angiogenesis. These multiple cells exhibit stochastic motion a...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2013
|
| Online Access: | http://hdl.handle.net/1721.1/77090 https://orcid.org/0000-0003-3155-6223 |
| _version_ | 1811093199001747456 |
|---|---|
| author | Ong, Lee Ling Ang, Marcelo H. Asada, Harry |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Ong, Lee Ling Ang, Marcelo H. Asada, Harry |
| author_sort | Ong, Lee Ling |
| collection | MIT |
| description | This paper describes an automated visual tracking system combining time-lapse and end-point confocal microscopy to aid the interpretations of cell behaviors and interactions, with the focus on understanding the sprouting mechanism during angiogenesis. These multiple cells exhibit stochastic motion and are subjected to photo-bleaching and the images acquired are of low signal to noise ratio. Hence, following time-lapse imaging, high resolution end-point images are acquired. Our approach applies a probabilistic motion filter (a backward Kalman filtering followed by track smoothing) which incorporates end-point and all available time-lapse information in a mathematically consistent manner to obtain trajectory and phenotype information of multiple individual cells simultaneously. An extension of this algorithm, track smoothing with a Multiple Hypothesis Testing (MHT) data association, is proposed to improve association of multiple close contact and proliferating cells across images acquired from different time points to existing track trajectories. Our methodology was applied to tracking endothelial cell sprouting in three-dimensional micro-fluidic devices. |
| first_indexed | 2024-09-23T15:41:14Z |
| format | Article |
| id | mit-1721.1/77090 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:41:14Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/770902022-10-02T03:23:39Z Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters Ong, Lee Ling Ang, Marcelo H. Asada, Harry Massachusetts Institute of Technology. Department of Mechanical Engineering Singapore-MIT Alliance in Research and Technology (SMART) Asada, Harry Ong, Lee Ling This paper describes an automated visual tracking system combining time-lapse and end-point confocal microscopy to aid the interpretations of cell behaviors and interactions, with the focus on understanding the sprouting mechanism during angiogenesis. These multiple cells exhibit stochastic motion and are subjected to photo-bleaching and the images acquired are of low signal to noise ratio. Hence, following time-lapse imaging, high resolution end-point images are acquired. Our approach applies a probabilistic motion filter (a backward Kalman filtering followed by track smoothing) which incorporates end-point and all available time-lapse information in a mathematically consistent manner to obtain trajectory and phenotype information of multiple individual cells simultaneously. An extension of this algorithm, track smoothing with a Multiple Hypothesis Testing (MHT) data association, is proposed to improve association of multiple close contact and proliferating cells across images acquired from different time points to existing track trajectories. Our methodology was applied to tracking endothelial cell sprouting in three-dimensional micro-fluidic devices. Singapore–MIT Alliance for Research and Technology (Bio-Systems and Micromechanics Interdisciplinary Research Group (IRG)) 2013-02-14T20:41:25Z 2013-02-14T20:41:25Z 2010-08 2010-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-7029-7 2160-7508 INSPEC Accession Number: 11466659 http://hdl.handle.net/1721.1/77090 Ong, Lee-Ling S., Marcelo H. Ang, and H. Harry Asada. “Tracking of Cell Population from Time Lapse and End Point Confocal Microscopy Images with Multiple Hypothesis Kalman Smoothing Filters.” 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW), San Francisco, USA. June 13-18, 2010. IEEE, 2010. 71–78. Web. ©2010 IEEE. https://orcid.org/0000-0003-3155-6223 en_US http://dx.doi.org/10.1109/CVPRW.2010.5543444 Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, (CVPRW) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Ong, Lee Ling Ang, Marcelo H. Asada, Harry Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title | Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title_full | Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title_fullStr | Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title_full_unstemmed | Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title_short | Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters |
| title_sort | tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis kalman smoothing filters |
| url | http://hdl.handle.net/1721.1/77090 https://orcid.org/0000-0003-3155-6223 |
| work_keys_str_mv | AT ongleeling trackingofcellpopulationfromtimelapseandendpointconfocalmicroscopyimageswithmultiplehypothesiskalmansmoothingfilters AT angmarceloh trackingofcellpopulationfromtimelapseandendpointconfocalmicroscopyimageswithmultiplehypothesiskalmansmoothingfilters AT asadaharry trackingofcellpopulationfromtimelapseandendpointconfocalmicroscopyimageswithmultiplehypothesiskalmansmoothingfilters |