Improving Segmentation and Registration of the Placenta in BOLD MRI

Blood Oxygen Level Dependent (BOLD) MRI images are used to study placental oxygen transport. To analyze the time series dataset of BOLD MRI images of the whole uterus for placental function, we need to segment the placenta in the images and register the images to a common template. In the following thesis, we primarily aim to explore deep neural networks to improve segmentation and registration of placental MRI images. Much of the work that is being done in this area is for the brain. But the placenta, unlike the brain, lacks a definite structure. The placenta also undergoes more deformations due to maternal and fetal motions and contractions. We aim to adapt, extend and modify the neural networks for the placenta specific problems.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Das, Haimoshri

Advisor dc:contributor.advisor

• Golland, Polina

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151416

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151416