Automated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-Lapse Microscopy

2006 ◽  
Vol 53 (4) ◽  
pp. 762-766 ◽  
Author(s):  
X. Chen ◽  
X. Zhou ◽  
S.T.C. Wong
Keyword(s):  
Cancer Cell ◽  
Time Lapse ◽  
Automated Segmentation ◽  
Cell Nuclei ◽  
Time Lapse Microscopy

scholarly journals ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

2020 ◽  
Vol 10 (18) ◽  
pp. 6187
Author(s):  
Leonardo Rundo ◽  
Andrea Tangherloni ◽  
Darren R. Tyson ◽  
Riccardo Betta ◽  
Carmelo Militello ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Lapse ◽  
Cell Counting ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Similarity Coefficients ◽  
Laboratory Practice ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Detection And Counting

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

scholarly journals ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

2020 ◽  
Author(s):  
Leonardo Rundo ◽  
Andrea Tangherloni ◽  
Darren R. Tyson ◽  
Riccardo Betta ◽  
Carmelo Militello ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Lapse ◽  
Cell Counting ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Similarity Coefficients ◽  
Laboratory Practice ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Detection And Counting

AbstractAdvances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils

2015 ◽  
Vol 20 (1) ◽  
pp. 34-51 ◽  
Author(s):  
Susanne Brandes ◽  
Zeinab Mokhtari ◽  
Fabian Essig ◽  
Kerstin Hünniger ◽  
Oliver Kurzai ◽  
...  
Keyword(s):  
Time Lapse ◽  
Polymorphonuclear Neutrophils ◽  
Automated Segmentation ◽  
Time Lapse Microscopy ◽  
Rigid Objects ◽  
Segmentation And Tracking

Automated segmentation of cancer cell nuclei in complex tissue sections

2001 ◽  
Author(s):  
Constantinos G. Loukas ◽  
George D. Wilson ◽  
Borivoj Vojnovic
Keyword(s):  
Cancer Cell ◽  
Automated Segmentation ◽  
Cell Nuclei ◽  
Tissue Sections

Experience of using time-lapse microscopy in IVF and ICSI programs

2020 ◽  
pp. 47-50
Author(s):  
N. V. Saraeva ◽  
N. V. Spiridonova ◽  
M. T. Tugushev ◽  
O. V. Shurygina ◽  
A. I. Sinitsyna
Keyword(s):  
Pregnancy Rate ◽  
Embryo Transfer ◽  
Selection Procedure ◽  
Time Lapse ◽  
Single Embryo Transfer ◽  
Main Group ◽  
Clinical Pregnancy ◽  
Control Group ◽  
Time Lapse Microscopy

In order to increase the pregnancy rate in the assisted reproductive technology, the selection of one embryo with the highest implantation potential it is very important. Time-lapse microscopy (TLM) is a tool for selecting quality embryos for transfer. This study aimed to assess the benefits of single-embryo transfer of autologous oocytes performed on day 5 of embryo incubation in a TLM-equipped system in IVF and ICSI programs. Single-embryo transfer following incubation in a TLM-equipped incubator was performed in 282 patients, who formed the main group; the control group consisted of 461 patients undergoing single-embryo transfer following a traditional culture and embryo selection procedure. We assessed the quality of transferred embryos, the rates of clinical pregnancy and delivery. The groups did not differ in the ratio of IVF and ICSI cycles, average age, and infertility factor. The proportion of excellent quality embryos for transfer was 77.0% in the main group and 65.1% in the control group (p = 0.001). In the subgroup with receiving eight and less oocytes we noted the tendency of receiving more quality embryos in the main group (р = 0.052). In the subgroup of nine and more oocytes the quality of the transferred embryos did not differ between two groups. The clinical pregnancy rate was 60.2% in the main group and 52.9% in the control group (p = 0.057). The delivery rate was 45.0% in the main group and 39.9% in the control group (p > 0.050).

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