{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/122993"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/122993","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Bootstrapping fully-automatic temporal fetal brain segmentation in volumetric MRI time series","abstract":"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","abstract_html":"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","abstract_has_math":false,"creators":["Zhang, Lawrence,M. Eng.Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Polina Golland."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:22:26Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/122993"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 41-42)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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"]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Bootstrapping fully-automatic temporal fetal brain segmentation in volumetric MRI time series"]}]}],"canonical_facts":{"dc:contributor.advisor":["Polina Golland."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Zhang, Lawrence,M. Eng.Massachusetts Institute of Technology."],"dc:date.accessioned":["2019-11-22T00:00:39Z"],"dc:date.available":["2019-11-22T00:00:39Z"],"dc:date.issued":["2019"],"dc:description":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 41-42)."],"dc:description.abstract":["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"],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/122993"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Bootstrapping fully-automatic temporal fetal brain segmentation in volumetric MRI time series"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:22:26Z"}