Automatic three‐dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images
Author:
Affiliation:
1. Department of Information & Computer Engineering Chung Yuan Christian University ROC Chung‐Li Taiwan
2. RIKEN Center for Advanced Photonics Wako Japan
3. RIKEN BioResource Research Center Tsukuba Japan
Funder
Ministry of Science and Technology, Taiwan
RIKEN
Publisher
Wiley
Subject
Histology,Pathology and Forensic Medicine
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/jmi.12949
Reference37 articles.
1. Optimal cut in minimum spanning trees for 3-D cell nuclei segmentation
2. Capturing Totipotent Stem Cells
3. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
4. Functional Heterogeneity of Embryonic Stem Cells Revealed through Translational Amplification of an Early Endodermal Transcript
5. Automated Detection and Tracking of Cell Clusters in Time-Lapse Fluorescence Microscopy Images
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3. U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images;2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2022-07-11
4. High Resolution U-Net for Quantitatively Analyzing Early Spatial Patterning of Human Induced Pluripotent Stem Cells on Micropatterns;2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2021-11-01
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