Riviere, BeatriceFuentes, David2023-08-092023-08-092023-052023-06-08May 2023Balsells, 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>.https://hdl.handle.net/1911/115054Machine 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.application/pdfengCopyright 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.Machine learningconvolutional neural networksinteractivesemi-automatic image segmentationbrain tumorsgliomaThesis2023-08-09