scholarly journals TLM-Converter: reorganization of long time-lapse microscopy datasets for downstream image analysis

BioTechniques ◽  
2011 ◽  
Vol 51 (1) ◽  
Author(s):  
Wee Choo Puah ◽  
Leong Poh Cheok ◽  
Maté Biro ◽  
Wee Thong Ng ◽  
Martin Wasser
Keyword(s):  
Image Analysis ◽  
Time Lapse ◽  
Time Lapse Microscopy ◽  
Long Time

scholarly journals Quantification of Mouse Hematopoietic Progenitors’ Formation Using Time-lapse Microscopy and Image Analysis

BIO-PROTOCOL ◽  
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Isabelle Bergiers ◽  
Christian Tischer ◽  
Özge Bölükbaşı ◽  
Christophe Lancrin
Keyword(s):  
Image Analysis ◽  
Time Lapse ◽  
Hematopoietic Progenitors ◽  
Time Lapse Microscopy

scholarly journals An Advanced Framework for Time-lapse Microscopy Image Analysis

2020 ◽  
Author(s):  
Qibing Jiang ◽  
Praneeth Sudalagunta ◽  
Mark B. Meads ◽  
Khandakar Tanvir Ahmed ◽  
Tara Rutkowski ◽  
...  
Keyword(s):  
Image Analysis ◽  
Drug Response ◽  
Time Lapse ◽  
Response To Therapy ◽  
Patient Specific ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Link Type ◽  
Microscopy Image ◽  
Microscopy Image Analysis

ABSTRACTTime-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients.Availability and ImplementationSource code is available at: https://github.com/CompbioLabUCF/CellTrackACM Reference FormatQibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

Time lapse microscopy observation of cellular structural changes and image analysis of drug treated cancer cells to characterize the cellular heterogeneity

2014 ◽  
Vol 30 (6) ◽  
pp. 724-734 ◽  
Author(s):  
Periasamy S. Vaiyapuri ◽  
Alshatwi A. Ali ◽  
Akbarsha A. Mohammad ◽  
Jeyalakshmi Kandhavelu ◽  
Meenakshisundaram Kandhavelu
Keyword(s):  
Image Analysis ◽  
Cancer Cells ◽  
Structural Changes ◽  
Time Lapse ◽  
Cellular Heterogeneity ◽  
Microscopy Observation ◽  
Time Lapse Microscopy

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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