Making computer vision Methods accessible for cell classification
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/121894 |
| _version_ | 1811092720409640960 |
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| author | Hung, Jane Yen. |
| author2 | Anne E. Carpenter and J. Christopher Love. |
| author_facet | Anne E. Carpenter and J. Christopher Love. Hung, Jane Yen. |
| author_sort | Hung, Jane Yen. |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018 |
| first_indexed | 2024-09-23T15:23:14Z |
| format | Thesis |
| id | mit-1721.1/121894 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:23:14Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1218942019-07-24T03:06:57Z Making computer vision Methods accessible for cell classification Hung, Jane Yen. Anne E. Carpenter and J. Christopher Love. Massachusetts Institute of Technology. Department of Chemical Engineering. Massachusetts Institute of Technology. Department of Chemical Engineering Chemical Engineering. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018 Cataloged from PDF version of thesis. Includes bibliographical references (pages 107-113). Computers are better than ever at extracting information from visual media like images, which are especially powerful in biology. The field of computer vision tries to take advantage of this fact and use computational algorithms to analyze image data and gain higher level understanding. Recent advances in machine learning such as deep learning based architectures have greatly expanded their potential. However, biologists often lack the training or means to use new software or algorithms, leading to slower or less complete results. This thesis focuses on developing different computer vision methods and software implementations for biological applications that are both easy to use and customizable. The first application is cardiomyocytes, which contain sarcomeric qualities that can be quantified with spectral analysis. Next, CellProfiler Analyst, an updated software application for interactive machine learning classification and feature analysis is described along with its use for classifying imaging flow cytometry data. Further software related advances include the first demonstration of a deep learning based model designed to classify biological images with a user-friendly interface. Finally, blood smear images of malaria-infected blood are examined using traditional machine learning based segmentation pipelines and using novel deep learning based object detection models. To entice further development of these types of object detection models, a software package for simpler object detection training and testing called Keras R-CNN is presented. The applications investigated here show how computer vision can be a viable option for biologists who want to take advantage of their image data. by Jane Yen Hung. Ph. D. Ph.D. Massachusetts Institute of Technology, Department of Chemical Engineering 2019-07-22T19:36:14Z 2019-07-22T19:36:14Z 2018 2018 Thesis https://hdl.handle.net/1721.1/121894 1103313692 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 113 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Chemical Engineering. Hung, Jane Yen. Making computer vision Methods accessible for cell classification |
| title | Making computer vision Methods accessible for cell classification |
| title_full | Making computer vision Methods accessible for cell classification |
| title_fullStr | Making computer vision Methods accessible for cell classification |
| title_full_unstemmed | Making computer vision Methods accessible for cell classification |
| title_short | Making computer vision Methods accessible for cell classification |
| title_sort | making computer vision methods accessible for cell classification |
| topic | Chemical Engineering. |
| url | https://hdl.handle.net/1721.1/121894 |
| work_keys_str_mv | AT hungjaneyen makingcomputervisionmethodsaccessibleforcellclassification |