Making computer vision Methods accessible for cell classification

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Hung, Jane Yen.

Advisor dc:contributor.advisor

• Anne E. Carpenter and J. Christopher Love.

• Chemical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121894

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121894