Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images
Cell migration studies in 3D environments become more popular, as cell behaviors in 3D are more similar to the behaviors of cells in a living organism (in vivo). We focus on the 3D angiogenic sprouting in microfluidic devices, where Endothelial Cells (ECs) burrow into the gel matrix and form solid l...
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| Format: | Article |
| Language: | en_US |
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SPIE
2017
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| Online Access: | http://hdl.handle.net/1721.1/107253 https://orcid.org/0000-0003-3155-6223 |
| _version_ | 1826214644014907392 |
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| author | Wang, MengMeng Ong, Lee-Ling Sharon Dauwels, Justin Asada, Haruhiko |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Wang, MengMeng Ong, Lee-Ling Sharon Dauwels, Justin Asada, Haruhiko |
| author_sort | Wang, MengMeng |
| collection | MIT |
| description | Cell migration studies in 3D environments become more popular, as cell behaviors in 3D are more similar to the behaviors of cells in a living organism (in vivo). We focus on the 3D angiogenic sprouting in microfluidic devices, where Endothelial Cells (ECs) burrow into the gel matrix and form solid lumen vessels. Phase contrast microscopy is used for long-term
observation of the unlabeled ECs in the 3D microfluidic devices. Two template matching based approaches are proposed to automatically detect the unlabeled ECs in the angiogenic sprouts from the acquired experimental phase contrast images. Cell and non-cell templates are obtained from these phase contrast images as the training data. The first approach applies. Partial Least Square Regression (PLSR) to find the discriminative features and their corresponding weight to distinguish cells and non-cells, whereas the second approach relies on Principal Component Analysis (PCA) to reduce the template feature dimension and Support Vector Machine (SVM) to find their corresponding weight. Through a sliding window manner, the cells in the test images are detected. We then validate the detection accuracy by comparing the results with the same images acquired with a confocal microscope after cells are fixed and their nuclei are stained. More accurate numerical results are obtained for approach I (PLSR) compared to approach II (PCA & SVM) for cell detection. Automatic cell detection will aid in the understanding of cell migration in 3D environment and in turn result in a better understanding of angiogenesis. |
| first_indexed | 2024-09-23T16:09:04Z |
| format | Article |
| id | mit-1721.1/107253 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:09:04Z |
| publishDate | 2017 |
| publisher | SPIE |
| record_format | dspace |
| spelling | mit-1721.1/1072532022-09-29T18:34:56Z Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images Wang, MengMeng Ong, Lee-Ling Sharon Dauwels, Justin Asada, Haruhiko Massachusetts Institute of Technology. Department of Mechanical Engineering Asada, Haruhiko Cell migration studies in 3D environments become more popular, as cell behaviors in 3D are more similar to the behaviors of cells in a living organism (in vivo). We focus on the 3D angiogenic sprouting in microfluidic devices, where Endothelial Cells (ECs) burrow into the gel matrix and form solid lumen vessels. Phase contrast microscopy is used for long-term observation of the unlabeled ECs in the 3D microfluidic devices. Two template matching based approaches are proposed to automatically detect the unlabeled ECs in the angiogenic sprouts from the acquired experimental phase contrast images. Cell and non-cell templates are obtained from these phase contrast images as the training data. The first approach applies. Partial Least Square Regression (PLSR) to find the discriminative features and their corresponding weight to distinguish cells and non-cells, whereas the second approach relies on Principal Component Analysis (PCA) to reduce the template feature dimension and Support Vector Machine (SVM) to find their corresponding weight. Through a sliding window manner, the cells in the test images are detected. We then validate the detection accuracy by comparing the results with the same images acquired with a confocal microscope after cells are fixed and their nuclei are stained. More accurate numerical results are obtained for approach I (PLSR) compared to approach II (PCA & SVM) for cell detection. Automatic cell detection will aid in the understanding of cell migration in 3D environment and in turn result in a better understanding of angiogenesis. Singapore. National Research Foundation (Singapore-MIT Alliance in Research and Technology (SMART). BioSystems & Micromechanics IRG) 2017-03-09T17:23:30Z 2017-03-09T17:23:30Z 2015-03 Article http://purl.org/eprint/type/ConferencePaper 9781628415032 http://hdl.handle.net/1721.1/107253 Wang, MengMeng et al. “Automatic Detection of Endothelial Cells in 3D Angiogenic Sprouts from Experimental Phase Contrast Images.” Ed. Sébastien Ourselin and Martin A. Styner. N.p., 2015. 94132I. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) https://orcid.org/0000-0003-3155-6223 en_US http://dx.doi.org/10.1117/12.2081819 Proceedings of SPIE--the Society of Photo-Optical Instrumentation Engineers 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 SPIE SPIE |
| spellingShingle | Wang, MengMeng Ong, Lee-Ling Sharon Dauwels, Justin Asada, Haruhiko Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title | Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title_full | Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title_fullStr | Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title_full_unstemmed | Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title_short | Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images |
| title_sort | automatic detection of endothelial cells in 3d angiogenic sprouts from experimental phase contrast images |
| url | http://hdl.handle.net/1721.1/107253 https://orcid.org/0000-0003-3155-6223 |
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