Bootstrapping fully-automatic temporal fetal brain segmentation in volumetric MRI time series

We present a method for bootstrapping training data for the task of segmenting fetal brains in volumetric MRI time series data. Temporal analysis of MRI images requires accurate segmentation across frames, despite large amounts of unpredictable motion. We use the predicted segmentations of a baseline model and leverage anatomical structure of the fetal brain to automatically select the "good frames" that have accurate segmentations. We use these good frames to bootstrap further model training. We also introduce a novel temporal segmentation model that predicts segmentations using a history of previous segmentations, thus utilizing the temporal nature of the data. Our results show that these two approaches do not provide conclusive improvements to the quality of segmentations. Further exploration into the automatic choice of good frames is needed before

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

2019

Author dc:creator

• Zhang, Lawrence,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Polina Golland.

• Electrical Engineering and Computer Science.

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/1721.1/122993

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122993