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Title:Brain tumor segmentation with multimodal magnetic resonance imaging
Author(s):Wang, Bo
Advisor(s):Liang, Zhi-Pei
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):MRI
image segmentation
brain tumor
CNN
deep-learning
Abstract:A brain tumor is an abnormal cell population that occurs in the brain. Identifying the abnormal region is one of the significant steps for patients with a brain tumor. However, manual labeling is subjective and time-consuming. For the patients to get an accurate diagnosis and timely surgery, an accurate and efficient automatic brain tumor detection and segmentation algorithm is necessary. For medical images, with limited data and a high requirement for accuracy, the traditional learning algorithms are currently insufficient. We follow the idea of most state-of-the-art approaches with multimodal brain tumor image segmentation, taking advantage of different modalities from magnetic resonance imaging (MRI) which can provide different texture features of the same brain image sample. However, most open-source brain tumor MRI images are recorded by modalities T1, T2, and FLAIR. In this thesis, we include a new modality of MRI imaging into existing modalities: Magnetization-Prepared Rapid Gradient Echo (MPRAGE). By including additional MPRAGE modality and applying corresponding input-level multimodal segmentation fusion strategy, we can enrich the image information for each single case and provide more intensity or texture information on specific regions of abnormal tissues to corresponding computing systems. With a comprehensive comparison of different segmentation models and different modalities usage, we can prove the improvement in efficiency and performance brought by MPRAGE modality in different multimodal brain tumor segmentation approaches.
Issue Date:2021-04-26
Type:Thesis
URI:http://hdl.handle.net/2142/110723
Rights Information:Copyright 2021 Bo Wang
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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