| dc.contributor.advisor | Golland, Polina | |
| dc.contributor.author | Das, Haimoshri | |
| dc.date.accessioned | 2023-07-31T19:38:06Z | |
| dc.date.available | 2023-07-31T19:38:06Z | |
| dc.date.issued | 2023-06 | |
| dc.date.submitted | 2023-06-06T16:35:44.129Z | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/151416 | |
| dc.description.abstract | 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. | |
| dc.publisher | Massachusetts Institute of Technology | |
| dc.rights | In Copyright - Educational Use Permitted | |
| dc.rights | Copyright retained by author(s) | |
| dc.rights.uri | https://rightsstatements.org/page/InC-EDU/1.0/ | |
| dc.title | Improving Segmentation and Registration of the
Placenta in BOLD MRI | |
| dc.type | Thesis | |
| dc.description.degree | M.Eng. | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| mit.thesis.degree | Master | |
| thesis.degree.name | Master of Engineering in Electrical Engineering and Computer Science | |
