Interactive Brain Tumor Segmentation

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dc.contributor.advisorRiviere, Beatriceen_US
dc.contributor.advisorFuentes, Daviden_US
dc.creatorBalsells, Citoen_US
dc.date.accessioned2023-08-09T14:24:46Zen_US
dc.date.available2023-08-09T14:24:46Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-06-08en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-08-09T14:24:46Zen_US
dc.description.abstractMachine learning based image segmentation relies on having access to a large dataset of labeled scans. Challenges arise when a sufficient training dataset is not available. To build a labeled dataset, one can manually create segmentations either by hand or assisted by a semi-automatic interactive tool. Here, interaction is given by the user in form of foreground and background clicks on the image. This thesis evaluates semi-automatic interactive image segmentation models applied to 3D brain tumor segmentation from MRI scans under two conditions. The first condition involves training models on the entire dataset. This provides a baseline for the best expected performance for each model. The second condition involves training models on portions of the dataset. This is done in an effort to model a dataset being gradually created from a small dataset. In both conditions, we find that the Wilcoxon signed rank test indicates significant results when comparing some of our interactive models with their fully-automatic counterpart. However, we ultimately deem the difference to be clinically irrelevant.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBalsells, Cito. "Interactive Brain Tumor Segmentation." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/115054">https://hdl.handle.net/1911/115054</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/115054en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectMachine learningen_US
dc.subjectconvolutional neural networksen_US
dc.subjectinteractiveen_US
dc.subjectsemi-automatic image segmentationen_US
dc.subjectbrain tumorsen_US
dc.subjectgliomaen_US
dc.subjecten_US
dc.titleInteractive Brain Tumor Segmentationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputational and Applied Mathematicsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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