Withdraw
Loading…
Time-lapse study of neural networks using phase imaging with computational specificity (PICS)
Kim, Eunjae
Content Files

Loading…
Download Files
Loading…
Download Counts (All Files)
Loading…
Edit File
Loading…
Permalink
https://hdl.handle.net/2142/107234
Description
- Title
- Time-lapse study of neural networks using phase imaging with computational specificity (PICS)
- Author(s)
- Kim, Eunjae
- Contributor(s)
- Popescu, Gabriel
- Issue Date
- 2020-05
- Keyword(s)
- Quantitative phase imaging
- Gradient light interference microscopy
- Time-lapse microscopy
- Neuronal growth analysis
- Machine learning
- Semantic segmentation
- Date of Ingest
- 2020-06-11T18:46:02Z
- Abstract
- In life sciences, fluorescent labeling techniques are used to study molecular structures and interactions of cells. However, this type of cell imaging has its own limitations, one of which is that the process of staining the cells could be toxic to the cells and possibly damage them. We are specifically interested in time-lapse imaging of live neurons to study their growth and proliferation. Neurodegenerative diseases are characterized by phenotypic differences in neuron growth and arborization. This thesis proposes a label-free digital staining method using the deep convolutional neural network to address the issues with the previous cell imaging method. Our results show that a deep neural network, when trained on phase images with correct fluorescent labels, can correctly learn the necessary morphological information to successfully predict MAP2 and Tau labels. This inference, in turn, allows us to classify axons from dendrites in live, unlabeled neurons.
- Type of Resource
- text
- Genre of Resource
- other
- Language
- en
- Permalink
- http://hdl.handle.net/2142/107234
Owning Collections
Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate researchManage Files
Loading…
Edit Collection Membership
Loading…
Edit Metadata
Loading…
Edit Properties
Loading…
Embargoes
Loading…