- Title Information
- Title
- Image Segmentation of Fluorescence Retinal Images for Photothermal Retinal Prosthetics
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Reddi, Anoop
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Lee, Jonghwan
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Borton, Dave
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Mathiowitz, Edith
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Morgan, Jeffrey
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Biology and Medicine: Biomedical Engineering
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2023
- Physical Description
- Extent
- , None p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Sc. M.)--Brown University, 2023
- Genre (aat)
- theses
- Abstract
- Currently, electrode-based retinal implants are severely limited in the restoration of vision loss. These implants are highly-invasive, limited in resolution, and degrade in utility overtime. A novel, minimally-invasive retinal prosthesis is being developed to overcome such limitations and stimulate retinal neurons to restore vision in blindness. This system uses gold nanorods (AuNRs) and near-infrared (NIR) light to activate retinal neurons by causing temperature changes in the neuronal membranes. A custom experimental setup involving a scanning laser system and a fluorescence imaging system will be used to validate the photothermal activation of retinal neurons ex-vivo before moving on to in-vivo investigations. An important aim in the ex-vivo validation is to determine the number of RGCs activated per unit time by analyzing the images taken by the camera. Our imaging system captures ~1000 retinal neurons, and it is very challenging if not impossible, to manually identify individual cells and obtain their time traces, especially when we have tens of retinal samples and data to analyze. This thesis focuses on implementing various image segmentation algorithms that automatically identify individual cells from a grayscale fluorescence image of a retinal explant and comparing their performance.
- Subject
- Topic
- plasmonic retinal prosthetic
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20230207