Advanced level-set based multiple-cell segmentation and tracking in time-lapse fluorescence microscopy images

2008 ◽  
Author(s):  
Oleh Dzyubachyk ◽  
Wiro Niessen ◽  
Erik Meijering
Keyword(s):  
Fluorescence Microscopy ◽  
Level Set ◽  
Time Lapse ◽  
Cell Segmentation ◽  
Advanced Level ◽  
Multiple Cell ◽  
Microscopy Images ◽  
Segmentation And Tracking

Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy

2010 ◽  
Vol 29 (3) ◽  
pp. 852-867 ◽  
Author(s):  
O. Dzyubachyk ◽  
W.A. van Cappellen ◽  
J. Essers ◽  
W.J. Niessen ◽  
E. Meijering
Keyword(s):  
Fluorescence Microscopy ◽  
Level Set ◽  
Cell Tracking ◽  
Time Lapse ◽  
Advanced Level

Correction to “Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy” [Mar 10 852-867

2010 ◽  
Vol 29 (6) ◽  
pp. 1331-1331 ◽  
Author(s):  
Oleh Dzyubachyk ◽  
Wiggert A. van Cappellen ◽  
Jeroen Essers ◽  
Wiro J. Niessen ◽  
Erik Meijering
Keyword(s):  
Fluorescence Microscopy ◽  
Level Set ◽  
Cell Tracking ◽  
Time Lapse ◽  
Advanced Level

scholarly journals A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

2018 ◽  
Author(s):  
Lamees Nasser ◽  
Thomas Boudier
Keyword(s):  
Fluorescence Microscopy ◽  
Cell Tracking ◽  
Time Lapse ◽  
Shape Variation ◽  
Denoising Method ◽  
Nuclei Segmentation ◽  
Cellular Processes ◽  
Challenging Tasks ◽  
Microscopy Images ◽  
Segmentation And Tracking

ABSTRACTTime-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes,i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithms, based on sparse coding, that can both enhance very faint and noisy nuclei but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters,with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96.96% recall for C.elegans dataset. Besides, in Drosophila dataset, our method achieved very high recall (99.3%).

Toward Robust Fully 3D Filopodium Segmentation and Tracking in Time-Lapse Fluorescence Microscopy

2019 ◽  
Author(s):  
Martin Maska ◽  
Tereza Necasova ◽  
David Wiesner ◽  
Dmitry V. Sorokin ◽  
Igor Peterlik ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Time Lapse ◽  
Segmentation And Tracking
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